- Colorado is the top five states in the US for total oil and gas wells, with 46,876 gas wells and 7,771 oil wells by 2014 (EIA, 2015).
- Study by Pétron et al. (2014) based on aircraft measurements in May 2012: the Colorado state inventory for total VOCs emitted by oil and gas activities was at least a factor of two below actual measured emissions.
- The 2014 EPA National Emissions Inventory estimated that in Weld County, oil and gas contributes to 80% of total point source VOC emissions (U.S. EPA, 2014).
- Studies by Gilman et al. (2013) and Eisele et al. (2009) also indicate that oil and gas development is the primary VOC source by mass in Boulder and Weld counties and potentially a key contributor to summertime O3 exceedances. During the spring and summer of 2015, oil and gas related VOCs measured at the NOAA Boulder Atmospheric Observatory (BAO) Tower (located 20 miles north of Denver) accounted for 40–60% of VOC reactivity with hydroxyl radicals (Abeleira et al., 2017). Swarthout et al. (2013) measured VOC distributions at BAO tower during winter 2011 and concluded that the strongest source of VOCs was northeast of BAO, where the alkane pattern matched the signature of natural gas from the Wattenberg oil and gas field.
- Urban combustion was identified as the major VOC source south of BAO. The Front Range does have stringent air emissions regulations for most oil and gas operations; in 2014 the State passed rules mandating detection and fixing of methane leaks and a 95% capture of well pad emissions, specifically with the goal of controlling O3production due to oil and gas VOCs (Code of Colorado Regulations, 2016). Enforcement of some of these regulations began May 2014, but during the measurement campaign when this study took place they might not have been fully implemented. It is possible that not all emission mitigation has been as effective as required and continuous monitoring of emissions from oil and gas related activities is necessary to evaluate compliance (U.S. EPA, 2015).
- There is significant non-combustion contribution to OA in the Front Range. The mean concentration of OA in plumes with a high influence of oil and natural gas (O&G) emissions was ∼ 40% higher than in urban-influenced plumes.
- By including emissions from the O&G sector using the top-down approach, it was estimated that the O&G sector contributed to < 5% of total OA, but up to 38% of anthropogenic SOA (aSOA) in the region. The best agreement between the measured and simulated median OA was achieved by limiting the extent of biogenic hydrocarbon aging and consequently biogenic SOA (bSOA) production. Despite a lower production of bSOA in this scenario, contribution of bSOA to total SOA remained high at 40–54%.
Sources and characteristics of summertime organic aerosol in the Colorado Front Range: perspective from measurements and WRF-Chem modeling
Roya Bahreini1,2, Ravan Ahmadov3,4, Stu A. McKeen3,4, Kennedy T. Vu2, Justin H. Dingle2,Eric C. Apel5, Donald R. Blake6, Nicola Blake6, Teresa L. Campos5, Chris Cantrell7, Frank Flocke5,Alan Fried8, Jessica B. Gilman3, Alan J. Hills5, Rebecca S. Hornbrook5, Greg Huey9, Lisa Kaser5,Brian M. Lerner3,4,a, Roy L. Mauldin7, Simone Meinardi6, Denise D. Montzka5, Dirk Richter8,Jason R. Schroeder6,b, Meghan Stell5, David Tanner9, James Walega8, Peter Weibring8,and Andrew Weinheimer5
(Local/Colorado authors are highlighted)
1Department of Environmental Sciences, University of California, Riverside, CA 92521, USA
2Environmental Toxicology Graduate Program, University of California, Riverside, CA 92521, USA
3Earth System Research Laboratory, National Oceanic and Atmospheric Administration, Boulder, CO 80305, USA
4Cooperative Institute for Research in Environmental Sciences, University of Colorado, Boulder, CO 80301, USA
5Atmospheric Chemistry Observations and Modeling Laboratory, National Center for Atmospheric Research, Boulder, CO 80301, USA
6Department of Chemistry, University of California, Irvine, CA 92697, USA
7Department of Atmospheric and Oceanic Sciences, University of Colorado, Boulder, CO 80303, USA
8Institute for Arctic and Alpine Research, University of Colorado, Boulder, CO 80303, USA
9Department of Earth and Atmospheric Sciences, Georgia Institute of Technology, Atlanta, GA 30033, USA
anow at: Aerodyne Research, Inc., Billerica, MA 01821, USA
bnow at: NASA Langley Research Center, Newport News, VA 23666, USA
Received: 20 Jan 2018 – Discussion started: 24 Jan 2018 – Revised: 03 May 2018 – Accepted: 23 May 2018 – Published: 14 Jun 2018
Abstract. The evolution of organic aerosols (OAs) and their precursors in the boundary layer (BL) of the Colorado Front Range during the Front Range Air Pollution and Photochemistry Éxperiment (FRAPPÉ, July–August 2014) was analyzed by in situ measurements and chemical transport modeling. Measurements indicated significant production of secondary OA (SOA), with enhancement ratio of OA with respect to carbon monoxide (CO) reaching 0.085±0.003µgm−3 ppbv−1. At background mixing ratios of CO, up to ∼ 1.8µgm−3 background OA was observed, suggesting significant non-combustion contribution to OA in the Front Range. The mean concentration of OA in plumes with a high influence of oil and natural gas (O&G) emissions was ∼ 40% higher than in urban-influenced plumes. Positive matrix factorization (PMF) confirmed a dominant contribution of secondary, oxygenated OA (OOA) in the boundary layer instead of fresh, hydrocarbon-like OA (HOA). Combinations of primary OA (POA) volatility assumptions, aging of semi-volatile species, and different emission estimates from the O&G sector were used in the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) simulation scenarios. The assumption of semi-volatile POA resulted in greater than a factor of 10 lower POA concentrations compared to PMF-resolved HOA. Including top-down modified O&G emissions resulted in substantially better agreements in modeled ethane, toluene, hydroxyl radical, and ozone compared to measurements in the high-O&G-influenced plumes. By including emissions from the O&G sector using the top-down approach, it was estimated that the O&G sector contributed to < 5% of total OA, but up to 38% of anthropogenic SOA (aSOA) in the region. The best agreement between the measured and simulated median OA was achieved by limiting the extent of biogenic hydrocarbon aging and consequently biogenic SOA (bSOA) production. Despite a lower production of bSOA in this scenario, contribution of bSOA to total SOA remained high at 40–54%. Future studies aiming at a better emissions characterization of POA and intermediate-volatility organic compounds (IVOCs) from the O&G sector are valuable.
Degraded air quality is a serious health and environmental concern and is caused by direct emissions as well as secondary products formed in the atmosphere. Emission sources are often highly diverse, and their air quality impacts are a complex function of transport, mixing, and photochemical processes.This session invites studies of the photochemical evolution of multiple local sources of pollution and their impacts on air quality and photochemical oxidant formation. The session focuses on the interactions and impacts of varied emissions sources, including mixing urban pollution with emissions from oil and gas extraction activities or agricultural sources, the interaction of anthropogenic and natural emissions, and combinations of biogenic or wildfire emissions with anthropogenic emissions. We invite field campaign data analysis and modeling studies that assess these interactions and their relevance to air quality. Primary Convener: Gabriele Pfister, National Center for Atmospheric Research, Boulder, CO, United States
Conveners: Ann Marie G Carlton, Rutgers State University, New Brunswick, NJ, United States, Gregory J Frost, NOAA, Earth System Research Laboratory, Boulder, CO, United States and Patrick J Reddy, APCD-TSP-B1, Denver, CO, United States.
Science Team Meeting: 4-8 May 2015, UCAR Center Green, Boulder, Colorado.
The UCAR Connect educational page about FRAPPÉ has videos describing the experiment.
The EOL FRAPPE project page provides an overview and links.
The Field Catalog provides forecasting and real-time data and the Plan of the Day.
The Front Range Air Pollution and Photochemistry Éxperiment (FRAPPÉ) field campaign will take place from 7/16 to 8/16, 2014. This is a collaborative effort between the Colorado Department of Public Health and the Environment, the University of Colorado and Colorado State University, UC Berkeley, and other university collaborators, local projects and agencies including local school districts, NASA, NOAA, and NCAR. This mission will be closely coordinated with the NASA DISCOVER-AQ project, which has agreed to conduct their final aircraft deployment in the Colorado Front Range. DISCOVER-AQ brings three aircraft to Colorado. The NASA P-3 is instrumented for comprehensive in-situ measurements, the NASA King Air will measure aerosol parameters with a downward looking LIDAR as well as make integrated column measurements of some tracers. The NASA Falcon will carry the GeoTASO instrument which measures column amounts of a number of tracers as well. We will also be joined by the Mooney TLS aircraft, which will measure CH4and NMHC emissions at low altitude, bringing the number of research aircraft to five. Principal Investigators: Gabriele Pfister (NCAR), Frank Flocke (NCAR), Jim Crawford (NASA).
All of the observations that will be made during FRAPPÉ and DISCOVER-AQ are listed in this table of measurements. It demonstrates the impressive suite of measurements we have assembled for this mission.
We have received a grant from the State of Colorado in support of FRAPPÉ data collection for our university collaborators, including aircraft and ground instrumentation. NSF Lower Atmospheric Observing Facilities (LAOF) as well as the NSF Atmospheric Chemistry Program is supporting the C-130 deployment.
The 4-week intensive deployment of the aircraft is augmented by extensive ground-based activities at some of the established local measurement sites such as the BAO tower, Platte Valley, Niwot Ridge, Rocky Mountain National Park. We will fly the instrumented NCAR / NSF C-130 aircraft to provide complementary measurements during DISCOVER-AQ, characterizing the local to regional chemical environment including photochemistry, oxidant and aerosol formation and fate, flow and recirculation patterns and large-scale inflow. Please find downloadable documents and other information such as upload and installation schedules, logistics, etc.under the appropriate tabs on top of the page. Please email Gabriele Pfister and Frank Flocke for comments or questions.
NEWS: GeoTASO is joining DISCOVER-AQ and FRAPPÉ – click here to read the news article
Check out the UCAR Connect page on FRAPPÉ here
Check out the NCAR Education&Outreach site on FRAPPÉ here
Other recent references as well:
https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2016JD025265
https://www.atmos-chem-phys.net/18/8293/2018/acp-18-8293-2018.html
40% of our pollution in the Denver metro area is drift from leaking at fracking sites
The Front Range of Colorado contains the region lying east of the foothills of the Rocky Mountains, from north of Colorado Springs to north of Fort Collins, and east encompassing the Denver-Julesburg (D-J) Oil and Gas Basin. Larger municipalities in the Front Range include Denver, Boulder, Longmont, Fort Collins, and Greeley. The D-J Basin is the site of major oil and gas developments as well as agriculture (crops and cattle). Since 2007, the Front Range has been classified by the U.S. EPA as a non-attainment area for ozone (O3) due to its summertime exceedances of the National Ambient Air Quality Standard (NAAQS) for O3. The EPA regulates ground-level O3 by specifying that the fourth-highest daily maximum 8-hour time averaged mixing ratio, averaged across three consecutive years, may not exceed 75 ppb (U.S. EPA, 2013a). In 2015 the EPA lowered the NAAQS standard to 70 ppb which is likely to be exceeded by the Denver and northern Front Range (NFR) when the new nonattainment designation is released at the end of 2017 (CDPHE, 2015). For this study, unless stated otherwise, the NAAQS refers to the 75 ppb standard that was in place July–August 2014 during the FRAPPE/DISCOVER-AQ field campaign.
In 2014 Colorado was ranked the 5th highest state in the US for total number of oil and natural gas wells with 7,771 oil and 46,876 natural gas wells (EIA, 2015). Weld County, located between Denver and Greeley, is the most densely drilled region of the D-J Basin; from 2010 to 2014 annual oil production increased from 21 to 81 million barrels and annual gas production increased from 219 to 392 billion cubic feet in Weld County (COGCC, 2014a). Volatile organic compounds (VOCs) and nitrogen oxides (NOx) emitted by oil and gas extraction activities are surface O3 precursors. According to a study by Pétron et al. (2014) based on aircraft measurements in May 2012, the Colorado state inventory for total VOCs emitted by oil and gas activities was at least a factor of two below actual measured emissions. The 2014 EPA National Emissions Inventory estimated that in Weld County, oil and gas contributes to 80% of total point source VOC emissions (U.S. EPA, 2014). Studies by Gilman et al. (2013) and Eisele et al. (2009) also indicate that oil and gas development is the primary VOC source by mass in Boulder and Weld counties and potentially a key contributor to summertime O3 exceedances. During the spring and summer of 2015, oil and gas related VOCs measured at the NOAA Boulder Atmospheric Observatory (BAO) Tower (located 20 miles north of Denver) accounted for 40–60% of VOC reactivity with hydroxyl radicals (Abeleira et al., 2017). Swarthout et al. (2013) measured VOC distributions at BAO tower during winter 2011 and concluded that the strongest source of VOCs was northeast of BAO, where the alkane pattern matched the signature of natural gas from the Wattenberg oil and gas field. Urban combustion was identified as the major VOC source south of BAO. The Front Range does have stringent air emissions regulations for most oil and gas operations; in 2014 the State passed rules mandating detection and fixing of methane leaks and a 95% capture of well pad emissions, specifically with the goal of controlling O3production due to oil and gas VOCs (Code of Colorado Regulations, 2016). Enforcement of some of these regulations began May 2014, but during the measurement campaign when this study took place they might not have been fully implemented. It is possible that not all emission mitigation has been as effective as required and continuous monitoring of emissions from oil and gas related activities is necessary to evaluate compliance (U.S. EPA, 2015).
Methane, a major trace gas in fugitive emissions from oil and gas operations, only reacts to form O3 on longer time scales and thus is not a precursor at the regional scale (Fiore et al., 2008). But, methane is commonly used as a marker of non-methane hydrocarbon (NMHC) O3 precursor VOCs emitted from oil and gas operations. Ethane is an example of a NMHC O3precursor that is co-emitted with methane; elevated methane and ethane indicates the presence of oil and gas emissions (Helmig et al., 2016). Due to the relatively long lifetime of ethane (~2 months) it is not a major contributor to O3 production on short time scales, but it is co-emitted with other NMHCs that react faster in the atmosphere to form O3 (Helmig et al., 2016). Background methane levels measured by aircraft in the planetary boundary layer in the NFR were 1.846–1.876 ppm in May 2012 (Pétron et al., 2014) and the regional annual average background ethane mixing ratio reported by Thompson et al. (2014) was 1.29 ppb. Other major sources of methane in the Front Range include beef and dairy production, large landfills, and wastewater treatment plants (Pétron et al., 2014). Concentrated animal feeding operations (CAFOs), including cattle feedlots, are known sources of methane, nitrous oxide, and ammonia from manure waste management and enteric fermentation in cattle. Landfill emissions are constituted of approximately 50% methane, 45% carbon dioxide, and the balance a mixture of trace gases including small amounts of NMHCs (Czepiel et al., 1996). Both animal operations and landfills do not emit ethane or light alkanes. There are a substantial number of wastewater treatment plants in the Front Range that potentially emit carbon dioxide, methane, and nitrous oxide (Gupta and Singh, 2012). CAFOs, landfills, and wastewater treatment plants are relatively small sources of methane in Weld County compared to the 75% of methane emissions attributed to oil and gas operations by a study in May 2012 (Pétron et al., 2014).
Inventory estimates for Colorado in 2014 attributed 40% of total NOx emissions to vehicles while petroleum and related industries were estimated to contribute 14% of NOx emissions (U.S. EPA, 2011; U.S. EPA, 2014). According to the National Emissions Inventory (NEI), statewide absolute NOx emissions from 2011 to 2014 decreased from 337,093 to 305,556 tons/yr. However NOx emissions in Weld County increased from 32,696 to 33,275 tons/yr during that same time period, mostly due to large increases in NOx from petroleum and related industries. The concurrent changes in VOC and NOx emissions could have large impacts on summertime O3 production in and downwind of the NFR; however, the relative contributions of these precursor sources to O3 levels throughout the region have not been well quantified. O3 production can occur in either NOx-limited or VOC-limited regimes. Highly trafficked urban areas that are saturated with NOx emissions from vehicles are often more limited in terms of O3 growth by VOC emissions, whereas more rural areas with high biogenic VOC emissions and low NOx levels are typically NOx-sensitive. The Front Range has low biogenic VOC emissions compared to other oil and gas-producing regions in the U.S. such as eastern Texas and Pennsylvania; consequently O3 production may be more sensitive to increases in VOCs from the local oil and gas operations emissions (McDuffie et al., 2016). Biogenic VOC levels are highly dependent on environmental conditions such as drought stress, and may contribute more significantly to overall VOC reactivity in the Front Range depending on the year (Abeleira et al., 2017). While rural areas are generally more NOx sensitive, there are circumstances where less urbanized areas can be VOC-limited. For example, Front Range inversions can trap Denver NOx emissions in the Platte River Valley (north of Denver) and cause the region to be more VOC-limited (Reddy, 2008).
Several studies have analyzed the O3 production resulting from oil and gas emissions in the Uintah Basin, Utah (Edwards et al., 2014; Schnell et al., 2016; Oltmans et al., 2016) as well as in the Upper Green River Basin, Wyoming (Schnell et al., 2009; Oltmans et al., 2014; Rappenglück et al., 2014; Field et al., 2015). However, the Colorado Front Range contains more complex land use patterns and population distributions than those areas. McDuffie et al. (2016) used an observationally constrained chemical box model to study the contributions of oil and gas VOCs to photochemical surface O3 production at the BAO Tower for two summer periods (July–August 2012 and 2014). They found that although on average, oil and gas VOCs contribute 2.9 ppb to daily maximum photochemical O3 at BAO; the contribution could be as high as 6 ppb on days with more sunlight and higher photolysis rates. Their conclusions identified a need for more spatially distributed studies in order to characterize the various sensitivities of O3 production in the Front Range, as well as to better understand the interactions of the complex mix of emission sources in the region. Another study used surface ozone and wind observations at the BAO Tower and a South Boulder monitoring site from 2009–2012 to identify potential transport of elevated ozone in the Front Range (Evans and Helmig, 2016). They found that 65% of one-hour averaged elevated ozone levels were associated with transport from areas of oil and gas operations, while only 9% of elevated ozone was correlated with winds from the Denver urban corridor. Both of these studies reported average contributions from oil and gas to O3production, a useful metric to help quantify the impact of oil and gas on surface O3, but not the only aspect to consider when evaluating the relevance of the issue to regulatory NAAQS compliance. Exceedances of EPA standards are based on the four highest 8-hour maxima throughout the year, not overall enhancement averaged over multiple days; therefore, it is important to study the impact oil and gas emissions can have on individual high O3 daily episodes to support regulatory efforts to comply with the NAAQS.
A major goal of this study is to examine individual high O3 episodes and evaluate the contributions of oil and gas precursors to O3 production using three case studies. The paper first provides an overview of surface O3 distribution in the NFR, specifically during the summer of 2014 when the FRAPPE/DISCOVER-AQ campaign took place. The overview includes a discussion of underlying O3 mixing ratios in the region, O3 daytime growth rates, surface wind patterns, a comparison of 2014 to other years, and a summary of O3 spatial variation at the surface sites in the NFR. The second half of the paper focuses on three case study dates during the FRAPPE/DISCOVER-AQ field campaign when there were mobile laboratory drives in addition to a variety of other surface measurements. The purpose of the case studies is to show different O3 formation conditions in a remote area in the northeast corner of the Denver-Julesburg-Wattenberg oil and gas field and to evaluate the influence of oil and gas emissions on specific high O3 days. Analysis of these case studies is a key step used in determining the potential sources of O3 precursors in order to evaluate the relative contributions of emissions sectors in the NFR to O3 growth.
Methods
Observations utilized in this study were gathered from fixed surface O3 monitoring sites, mobile laboratories, discrete air samples in flasks, and meteorological stations. An overview of the monitoring site locations as well as the different pollutant sources in the study region is shown in Figure 1. Although the Front Range includes the Denver Metropolitan area, this investigation was focused on the NFR from Boulder to Fort Collins and east.
Figure 1 Map of Front Range with monitoring sites and potential emission sources. Map of the Front Range showing monitoring sites and methane and potential O3 precursor sources (color coded dots) (COGCC, 2014b, D Wells, CDPHE, personal communication, July 2014). DOI: https://doi.org/10.1525/elementa.254.f1
Fixed surface O3 monitoring sites included three operated by the Colorado Department of Public Health and Environment (CDPHE), the BAO tall tower in east Erie operated by the National Oceanic and Atmospheric Administration Global Monitoring Division (NOAA/GMD), a site in Pawnee National Grassland operated by the US Forest Service, Rocky Mountain Research Station, a site near Platteville operated by the NOAA Chemical Sciences Division (CSD), and a site near Platteville operated by NASA/Goddard and the Pennsylvania State University. Two of the CDPHE sites are located in Fort Collins and the third is operated at the Weld County Tower site in Greeley. All data from the fixed sites were converted to hourly averages for this study and raw data are available in the data archive on the NASA DISCOVER-AQ website (NASA, 2015). Hourly averaged O3 data for June, July, and August of 2013 and 2014 at Platteville were obtained from the NOAA CSD who operated a surface O3 monitoring site from 2011 until August 29, 2014 (NOAA CSD, 2014). Reference instruments at the three CDPHE sites and the Pawnee Buttes site were calibrated in accordance with U.S. EPA protocols (U.S. EPA, 2013b). Surface O3 observations from the NOAA/GMD sites have undergone thorough evaluation and extensive quality control following calibration procedures available through the World Meteorological Organization (Galbally et al., 2013). A list of the seven surface O3 sites is provided in Table 1.
Table 1
Summary of fixed surface O3 monitoring sites. DOI: https://doi.org/10.1525/elementa.254.t1
| Site Name | Organization | Latitude (decimal degrees) | Longitude (decimal degrees) | Site Altitude (masl) | O3Measurement Inlet Height (magl) | O3 Analyzer (all instruments UV Absorption Analyzers) | Wind Data Available? |
|---|---|---|---|---|---|---|---|
|
|
|||||||
| BAO Tower | NOAA/GMD | 40.050° | –105.004° | 1584 | 6 | Thermo-Scientific Model 49C | Yes |
| Pawnee Buttes | US Forest Service, Rocky Mountain Research Station | 40.810° | –104.043° | 1658 | 2 | 2B Technologies Model 202 | No |
| Fort Collins – CSU | CDPHE | 40.571° | –105.080° | 1530 | 3 | Teledyne API E400 | Yes |
| Fort Collins – West | CDPHE | 40.593° | –105.141° | 1571 | 3 | Teledyne API E400 | No |
| Greeley | CDPHE | 40.386° | –104.737° | 1483 | 3 | Teledyne API E400 | Yes |
| Platteville | NASA/Goddard & Penn State University | 40.182° | –104.727° | 1523 | 4 | Thermo-Scientific Model 49C | Yes |
| Platteville | NOAA/CSD | 40.183° | –104.726° | 1523 | 10 | Thermo-Scientific Model 49C | No |
The mobile laboratory data were provided by the Aerodyne group, which used a 2B Tech. 205 to measure O3 (operated in accordance with federal method EQOA-0410-190), a LICOR to measure CO2, and Aerodyne tunable infrared laser direct absorption spectrometer based instruments (TILDAS) to measure methane, ethane, ammonia, nitrous oxide, and carbon monoxide (CO) (Herndon et al., 2005; McManus et al., 2015; Yacovitch et al., 2014). Sensitivity limits, noise statistics, calibration procedures, and quality assurance for the Aerodyne TILDAS are described in detail by Herndon et al. (2005), McManus et al. (2015), Yacovitch et al. (2014), and Yacovitch and Herndon (2014). Mobile laboratory O3measurements when in close proximity (<1 km) to the Greeley monitoring site are in good agreement (on average the differences are <3 ppb over a wide range of O3 mixing ratios) (see Supplemental Material Figures S1–S3). The sampling frequency of O3 was every 2 seconds and all other gases on the mobile laboratory platform were measured every second. Two 2D anemometers were mounted on the mobile laboratory (3.2 m above the ground and 1.6 m in front of the roof line) to measure wind speed and wind direction: an AIRMar 200WX (with built in GPS, 1 Hz logging frequency) that internally compensated for vehicle movement, and a RM Young (4 Hz logging frequency) coupled with a Hemisphere V103 GPS compass. True wind measurements from this tandem of instruments were determined using an algorithm (~3% uncertainty for one-minute averaged data while moving), and all meteorological data were filtered to exclude wind speeds below 2.5 m/s in accordance with methods used by Pétron et al. (2012). For use in this study, all mobile laboratory data were time-averaged to a one-minute resolution. The mobile laboratory data were filtered for wind direction and vehicle speed to exclude data that may have been impacted by its own vehicular emissions; for all vehicle velocities below 7 mph any measurements from the rear direction were removed.
Discrete halocarbon/hydrocarbon grab samples were collected by another mobile laboratory operated by a University of California Irvine (UCI) research group using stainless steel evacuated flasks. The samples were analyzed with both GC and GC/MS at UCI (Colman et al., 2001). Species of interest to this study included ethane, propane, n-butane, n-pentane, benzene, and isoprene. Air samples were taken at a variety of locations throughout the Front Range, detailed specifically in the case study section below.
Air mass backward trajectory analyses were completed using NOAA’s HYSPLIT atmospheric transport and dispersion modeling system (Stein et. al., 2015; Rolph et al., 2017). Five-hour back trajectories were calculated for each of the three case study dates at a height of 300 m agl using the North American Model (NAM) 12 km meteorological reanalysis. Ending times were selected to coincide with the timing of the mobile laboratory routes, and the five-hour length was chosen to encompass the period of potential photochemical O3 production. Ending locations of the trajectories were set to correspond in position to the location of the mobile laboratory at the time of the trajectory.
Results and discussion
Overview of surface O3 in the NFR
O3 growth
Before investigating the relative contributions of different pollution sources to higher O3production, it is important to estimate the median summertime level of O3 without significant photochemical production. For this purpose, Figure 2 is used to estimate the underlying O3 distribution on days when the O3 mixing ratios are minimally impacted by boundary layer photochemical production (peak less than 60 ppb). This value is representative of the O3 mixing ratio due to boundary layer mixing with the free troposphere, limited regional production, and average levels of titration of O3 with NO. Days with peak hourly O3 values <60 ppb are ~35% (28–44%, depending on the site) of all summer days in 2013, 2014, and 2015 (June–August) at the Front Range locations. The plot shows there is an increase in O3 from approximately 7:00 to 13:00 (all times in this study are reported in Mountain Daylight Time (MDT)) and the O3 mixing ratio levels out around 45–55 ppb in the afternoon. The morning growth rate falls in the range ~2.2–7.2 ppb/hr at the Front Range sites on lower O3 days.
Figure 2 Median O3 growth rates at six surface sites on low peak O3 days. Median O3 binned by hour of day for all days with peak mixing ratio less than 60 ppb including June, July, and August of 2013, 2014, and 2015 (Platteville only 2013 and 2014). The fraction of total days included are shown in the legend for each site. Growth rates estimated using linear fit lines (solid lines color coded by site) for period of constant O3 increase at each site. DOI: https://doi.org/10.1525/elementa.254.f2
Median summertime limited photochemical production O3 mixing ratios were estimated by examining values of O3 at Pawnee Buttes on days when the peak was less than 60 ppb (116 of 276 days). The Pawnee Buttes site was selected because it is representative of a less polluted area; normally it experiences lower daytime O3 growth rates and less O3 depletion at night from reaction with NO compared to the other surface monitoring sites. As Figure 2shows, the median daytime maximum O3 mixing ratio at Pawnee Buttes was 52 ppb, with 25th and 75th percentile values of 48 and 55 ppb, respectively. Based on these estimates, the O3 mixing ratio on days with limited photochemical production in the NFR region is determined to be within the range of approximately 45–55 ppb. O3 levels measured above this value are likely due to more significant photochemical production and can be enhanced by pollution sources such as oil and gas activities and urban emissions.
On days of high O3, the morning O3 growth continues into the afternoon and peaks around 15:00. This phenomenon is shown in Figure 3, which is similar to Figure 2 but contains only dates where the peak hourly mean O3 was ≥75 ppb. The growth rates are higher than the low-O3 days due to the simultaneous occurrence of boundary layer mixing and photochemical production; the growth also continues later into the day when photochemical production dominates the O3 growth. Growth rates were ~4.2–11 ppb per hour; about twice those under conditions represented in Figure 2. The growth rate at BAO was lower than at Platteville or Greeley by ~3 ppb/hr, which is significant as previous studies on VOCs from oil and gas activities and O3 production in the Front Range have based their findings on data from BAO Tower (Gilman et al., Swarthout et al., 2013; Evans and Helmig, 2016; McDuffie et al., 2016; Abeleira et al., 2017). Based on benzene measurements by Halliday et al. (2016), located more centrally in the gas field in Platteville, other areas in the Front Range are more impacted by oil and gas emissions than BAO. These results, in addition to Figure 3, show that O3 production at BAO is somewhat moderate compared to locations further towards the center of the D-J Basin such as Platteville. Previous studies based on data collected at BAO have likely not captured the maximum influence of oil and gas emissions on O3 production. In Figure 3, days with peak hourly values ≥75 ppb represent ~15% (10–22%) of all days through the summer, except at the minimally polluted Pawnee Buttes site where only 4% of days had values ≥75 ppb. The summer months of 2014 had fewer days than 2013 or 2015 with peak hourly values ≥75 ppb at the surface sites included in Figure 3, indicating that 2014 was lower than average summers in terms of high O3 episodes. These high O3 days are of particular interest since they represent potential exceedances of the NAAQS for O3. High O3 episodes may not follow the average production patterns reported by studies such as McDuffie et al. (2016), yet they are important when considering health effects and regulatory exceedances.
Figure 3 Median O3 growth rates at six surface sites on high peak O3 days. Median O3 binned by hour of day for all days with peak mixing ratio greater than 75 ppb including June, July, and August of 2013, 2014, and 2015 (Platteville only 2013 and 2014). The fraction of total days included are shown in the legend for each site. Growth rates estimated using linear fit lines (solid lines color coded by site) for period of constant O3 increase at each site. DOI: https://doi.org/10.1525/elementa.254.f3
Wind direction and surface O3
The local meteorology of the region is strongly influenced by its complex terrain setting in the lee of the Rocky Mountains. Diurnal, thermally driven flows are a common meteorological feature of the surface air in the Front Range, especially during the months of April through September (Losleben et al., 2000). Upslope flow is caused by rapid surface heating on eastward facing foothill and mountain slopes during the morning that leads to the warm air near the surface rising and forming winds from the east with a slight southerly component (Toth and Johnson, 1985; Watson et al., 1998). In the late afternoon, the pattern is reversed and the winds come from the west and go down the mountain slopes. Upslope flow earlier in the daytime has the potential to transport air pollutants out of the D-J Basin and throughout the Front Range; downslope flow overnight has the potential to transport pollutants back (Halliday et al., 2016).
The air circulation patterns throughout the Front Range influence the transport of O3precursors and consequently impact the O3 measured at the monitoring stations (Evans and Helmig, 2016). The plots shown in Figure 4 are polar histograms that display the O3mixing ratios based on wind directions at Fort Collins – CSU and Greeley from 5:00–10:00 (a, c) and 10:00–15:00 (b, d) including all days from July 16 to August 10, 2014 – the period of FRAPPE/Discover-AQ. The frequency of wind direction measurements is represented by the bar length and the O3 mole fractions are differentiated by colors. The wind direction is dominated by the upslope-downslope trends, with winds prevailing from the north and west from 5:00–10:00 and from the southeast and east from 10:00–15:00. At Fort Collins – CSU from 10:00–15:00, the winds display a clear pattern and originate in the southeast. Relative to the monitoring station, the winds are coming predominantly from the general direction of Platteville and the surrounding areas with dense oil, gas, and agricultural activities. Longer range transport of emissions from Denver, lying to the south-southeast of the monitoring station, and sources within Fort Collins, a city of over 150,000 inhabitants, were potential sources of urban O3 precursors. Greeley’s more central location in the Wattenberg oil and gas field and dominant easterly winds bring a mixture of O3precursors from urban, oil and gas, and agriculture during the time of peak photochemical O3 production.
Figure 4 FTC-CSU and Greeley polar O3 histograms. Polar histograms with O3 mole fractions based on wind direction at FTC-CSU from 5:00–10:00 (a) and 10:00–15:00 (b) and at Greeley from 5:00–10:00 (c) and 10:00–15:00 (d) for July 15 to August 10, 2014. O3 mixing ratios (in ppb) are indicated by color and the percentages represent the frequency of wind coming from a particular direction. DOI: https://doi.org/10.1525/elementa.254.f4
Summer 2014 surface O3
O3 measurements in Colorado have fluctuated above and below the 2008 NAAQS standard of 75 ppb, with an overall increasing trend since 2009, although 2014 and 2015 were slightly lower than previous years (CDPHE, 2016). The CDPHE Air Pollution Control Division states that the recent oil and gas development in Colorado, in addition to overall economic growth since 2010, may be a contributor to the measured O3 growth (CDPHE, 2016). The weather during the FRAPPE/DISCOVER-AQ study period of July and August, 2014 was relatively cool and damp, with high thunderstorm activity that can inhibit OH radical generation and O3 production even in the presence of adequate precursors for O3production (McDuffie et al., 2016). July 2014 was the 11th wettest July since 1872 and August 2014 was the 19th wettest August (NWS, 2015). Table 2 summarizes the O3 monitoring for 2013, 2014, and 2015 in metrics used to classify NAAQS exceedances. 2014 showed lower 1st8-hour maximums and 4th 8-hour maximums than 2013 and 2015 for all monitoring sites, demonstrating the impact the cool and wet conditions of summer 2014 had on photochemical O3 production.
Based on the 2014 4th maximum 8-hour average, the sites with the highest O3 were Rocky Flats – North, NREL, Chatfield State Park, and Ft. Collins – West. The first three of these sites are located west or south of Denver (see Figure 1); however, Ft. Collins is ~50 miles far north of the Denver metropolitan area and is less likely to be impacted by urban Denver emissions. The Greeley site, located in the Wattenberg oil and gas field in much closer proximity to Ft. Collins than Denver or Boulder, has a 3-year average value that is above the new 70 ppb NAAQS limit. This shows that the O3 levels across the Front Range are elevated in a variety of geographic settings and are likely impacted by several emissions sources – not just Denver urban emissions. The spatial differences between monitoring sites are demonstrated in Supplemental Material Figure S4 for the week of July 22 to July 28, 2014. Some dates, such as the 28th, have multiple sites above the 75 ppb threshold, whereas on the 27th, only the Platteville NOAA station reached mixing ratios above 75 ppb. High O3 production is sometimes relatively homogeneous throughout the region and sometimes influenced more locally by precursor emissions reaching a particular monitoring site.
Case studies
A detailed analysis of O3 and a variety of other gaseous species was performed on measurements taken during three selected days of the FRAPPE/DISCOVER-AQ study period. The gases that were measured continuously on the mobile laboratory platform included O3, carbon dioxide, CO, methane, ethane, nitrous oxide, ammonia, acetylene, and CO. Ethane and methane are co-emitted from oil and gas sources and not by biogenic methane sources. A strong correlation between them is indicative of an oil and gas source (Helmig et al., 2014). In the Northern Hemisphere, ethane is sourced predominantly from oil and gas activities and not from biofuel use or biomass burning; therefore, it is a suitable tracer for oil and gas sources (Xiao et al., 2008; Thompson et al., 2014; Helmig et al., 2016). During the sample collection phase of the study there was no significant biomass burning to contribute to the observed ethane levels. Additional oil and gas chemical tracers that were measured in flask samples are propane, benzene, n-butane, and n-pentane, each of which except for propane are co-emitted from vehicles and oil and gas activities. Oil and gas activity is the dominant source of propane in the NFR (Gilman et al., 2013; Thompson et al., 2014) and consequently many studies have utilized strong correlations of NMHCs with propane to indicate oil and gas sources of emissions (Pétron et al., 2012; Gilman et al., 2013; Swarthout et al., 2013; Thompson et al., 2014; Pétron et al., 2014; Halliday et al., 2016). Propane, benzene, n-butane, and n-pentane are not the most reactive O3 precursors compared to more common urban VOC mixtures, but previous studies have shown they can still dominate reactivity with the OH radical when present in the large abundances measured in the Front Range (Gilman et al., 2013; Swarthout et al., 2013; Abeleira et al., 2017). Isoprene is the most prevalent naturally occurring biogenic VOC in the NFR and the average daytime mixing ratio measured at the BAO Tower during the summer of 2015 was 0.2 ppb (Abeleira et al., 2017). CO is primarily a product of incomplete combustion and its strong association with urban pollution makes it an effective tracer of urban emission influences (Parrish, 2006; Té et al., 2012). However, there are additional regional sources of CO in the Front Range: according to emission inventory estimates, 51% of CO emissions in Weld County are emitted from highway and off-highway vehicles, 9% from fuel combustion, and 30% from petroleum and related industries (U.S. EPA, 2014). Acetylene is considered a suitable tracer of urban influence and vehicle exhaust since it is primarily emitted from automobiles (Whitby and Altwicker, 1978; Fortin et al., 2005; Pétron et al., 2012; Thompson et al., 2014). Therefore, high correlation of CO with acetylene is used in this study to identify vehicular sources for CO, while correlation with ethane implicates an oil and gas source for CO. Other chemical tracers such as ammonia, nitrous oxide, and propane along with methane are used to help attribute O3 precursors to their respective emissions sources – agriculture, wastewater treatment plants, and oil and gas production.
NOx measurements were not made as part of the mobile laboratory suite of observations. From 2011 to 2014, statewide NOx inventory emissions decreased but absolute NOx emissions in Weld County (16% highway traffic and 7% off-highway traffic in 2014) increased by 1.8% due to increases in petroleum and related industries that accounted for 54% of NOx emissions in 2014 (U.S. EPA, 2011; U.S. EPA, 2014). NOx was measured at BAO, Platteville, and Fort Collins-West on days of the case studies. Daytime NOx values at these sites fell in the range of 2–5 ppb with somewhat higher values at the Platteville site (2–20 ppb) later into the morning (NASA, 2015). McDuffie et al. (2016) demonstrated that in this region the O3 production efficiency is maximized for NOx mixing ratios of 1–2 ppb. The NOx observations from the fixed sites suggest that broadly through the NFR O3 precursor NOx values fall within a regime that would sustain ample O3 production.
Mobile laboratory data selected for this analysis were from July 23, August 3, and August 13, 2014, with the drives encompassing several regimes with varying potential for O3formation and particularly the impact of oil and gas related emissions. July 23 (Figures 5, 6, 7, 8) captured emissions in an area minimally affected by oil and gas emissions until the end of the drive when the measurements were taken in a more central location to oil and gas activities. August 3 (Figures 9, 10, 11, 12) was a high O3 day measured by the mobile laboratory as well as at multiple stationary O3 monitoring sites. Highly elevated ethane and methane measurements as well as decreasing CO levels implicated oil and gas operations as the major source of O3 precursors in the drive area. August 3 also showed high levels of ammonia and nitrous oxide relative to July 23, indicating the presence of agricultural methane emissions. The drive on August 13 (Figures 13, 14, 15) showed high localized O3levels in a rural area that were not seen at the surface O3 station in Greeley. The high O3 was coincident with elevated ethane and methane and low CO levels compared to the other drives, indicating that oil and gas sources contributed significantly to the elevated O3mixing ratios in the area. Ethane and O3 were not well correlated with points measured at the same time on any of the drives, but this type of instantaneous correlation was not expected since O3 levels reflect cumulative production from precursors over a period of time as reflected in the growth rates shown in Figure 3. Overall, concurrent enhancement of ethane and O3 throughout the drive was more indicative of oil and gas influence than point by point correlation.
Figure 5 Map of mobile laboratory O3 measurements on July 23, 2014. Map of O3 measured during the mobile laboratory drive on July 23. Times in red show the location of the mobile laboratory at that time and the black arrows indicate direction of travel. Five-hour back trajectories are shown by solid lines (blue arriving at 15:00 and red at 16:00) with hourly locations noted by outlined symbols. The portion of the drive circled in red was included in Figure 6 (note that two loops were completed within the circled area at the conclusion of the drive; O3 measurements shown were measured on the second loop). DOI: https://doi.org/10.1525/elementa.254.f5
July 23, 2014: Oil and gas emissons, moderate O3 levels
The drive on July 23, shown in Figure 5, started in Greeley at 10:00 (MDT) and travelled east and north, ending at 16:10. The O3 during this drive was low compared to the other drives, with the highest O3 measured at the end of the drive. The O3 was fairly constant around 50 ppb, with an abrupt increase to approximately 65 ppb occurring just before 16:00. Therefore, the majority of the O3 on the drive was approximately median summertime O3levels on days with limited photochemical production (~45–55 ppb) with a peak of 15 ppb of enhancement. Surface O3 levels measured at the stationary reference sites on the 23rdwere elevated at the BAO Tower and at Platteville, with hourly averages peaking above 75 ppb, but were lower at the other sites (see Supplemental Material Figure S5). Sites to the south of the mobile laboratory drive had stronger O3 enhancements on this day. As noted earlier in the discussion of the surface sites, there was a tendency for O3 enhancements to be localized in a portion of the NFR on particular days (see Supplemental Material Figure S1).
Methane, ethane, CO, and ammonia mixing ratios during the afternoon were all lower than during the other two drive days (see Figure 6) aside from increases at the very end of the drive. Nitrous oxide measurements showed a few moderate spikes but were mostly not elevated relative to the other drives.
Figure 6 Time series of gaseous species from mobile laboratory on July 23, 2014. Time series plots of gaseous species measured from a mobile laboratory during period of baseline O3 and O3 increase on July 23. Methane and ethane (a), CO and O3(b), methane and ammonia (c), and methane and nitrous oxide (d). DOI: https://doi.org/10.1525/elementa.254.f6
The weather at Greeley-Weld County airport on July 23 was cloudy in the morning but clear for the majority of the afternoon with no precipitation and a maximum temperature of 32°C (Weather Underground, 2015). Aside from some thin clouds, the afternoon was warm and sunny, suitable for photochemical O3 production. Winds during the drive came mostly from the east, and from 14:00–16:00 they were from the east and east-southeast (see Figure 7). Just before 16:00 the winds throughout the entire drive region shifted and came from the west for the remainder of the drive period (Weather Underground, 2014). Since most of the drive was located in the farthest northeast area of the oil and gas field, the winds were coming from an area with fewer wells and fewer pollutant sources such as CAFOs, landfills, and wastewater treatment facilities. Near the end of the drive at 15:55, concurrent with the time of the wind shift, as the mobile laboratory moved into the heart of the oil and gas field, O3 reached ~65 ppb, the highest measured values during the drive. The sudden increase in O3 when the winds shifted shows the presence of sharp spatial gradients in O3 in the middle of the oil and gas field. This O3 increase corresponded to elevated ethane amounts as well as spikes in methane, ammonia, and nitrous oxide a few minutes after the O3 increase. The methane spike overlapped well with ammonia and nitrous oxide, indicating a potential agriculture and wastewater treatment facility source. The O3 increase aligned more closely with the ethane increase, implicating oil and gas emissions as a significant source of the O3 VOC precursors. The back trajectories in Figure 5 show the modeled pathways of the air parcels that reached the drive route at 15:00 (blue solid line) and 16:00 (red solid line), both of which passed through the eastern edge of the gas field without traversing any large urban areas, confirming the mobile lab measurements that saw low urban emissions and modest oil and gas emissions until the increase at the end of the drive. The surface winds in Figure 7 do not appear to reflect the broader air parcel transport to the drive area. Differences between the surface winds and the back trajectories are possibly related to the difference in height; the trajectories originated 300 m above the ground compared to the mobile laboratory wind measurements taken 3.2 m above the ground.
Figure 7 Wind rose from mobile laboratory drive on July 23, 2014. Wind rose displaying mobile lab meteorological data during period of baseline O3 and O3 increase from 14:00–16:10 on July 23. Wind speed (in m/s) is indicated by color and the percentages represent the frequency of wind coming from a particular direction. DOI: https://doi.org/10.1525/elementa.254.f7
Flasks for hydrocarbon determinations were collected in Platteville and west of Denver near Golden (Latitude 39.7497, Longitude –105.1830) during the campaign. The flask data are plotted in Figure 8. The three flasks sampled at Platteville on July 23 had much higher mixing ratios of propane, ethane, benzene, n-butane, and n-pentane than any of the four flasks sampled at the Denver site on that same day. The high correlations of ethane, benzene, n-butane, and n-pentane with propane among the Platteville flasks exhibit the chemical signature of oil and gas emissions while the NMHCs in the Denver flasks were not highly correlated with propane. The propane levels measured in Platteville on July 23 (9.4–20.3 ppb) were very high relative to the annual average regional background level of 0.4 ppb (Thompson et al., 2014) and other cities and urban areas around the U.S., where typical daytime mixing ratios are in the range of 0.29–3.51 ppb (Baker et al., 2008). These high NMHC levels likely contributed to the 75–80 ppb peak O3 measured at the Platteville surface monitoring station. Although the trajectories and surface winds suggest the possible transport of oil and gas emissions to the mobile laboratory measurement location, the majority of the mobile laboratory drive (prior to 16:00) located northeast of Platteville did not measure high O3 or high O3 precursors from either oil and gas or urban sources. At Platteville the average daytime mixing ratio of isoprene measured in the flasks on July 23 was 0.04 ppb, well below the average mixing ratio of 0.2 ppb that was observed at the BAO Tower during summer 2015 (Abeleira et al., 2017). This demonstrates that on July 23, natural sources of VOCs did not contribute a significant amount to O3 production.
Figure 8 Mixing ratios measured in flasks collected on July 23, 2014. Mixing ratios measured in flasks collected on July 23 displaying coefficients of determination of ethane vs. propane (a), benzene vs. propane (b), n-butane vs. propane (c), and n-pentane vs. propane (d). DOI: https://doi.org/10.1525/elementa.254.f8
August 3, 2014: Mixed emissions, high O3 day
Figure 9 shows the drive route on August 3, beginning at 10:15 and ending at 18:00. The O3measurements during this drive were cut off around 13:00 before resuming at 15:30, but there was consistent O3 growth between 11:30 and 13:00 that reached 75–80 ppb (approximately 20–30 ppb above median mixing ratios on low photochemical production days) by the time the interruption in measurements occurred. High O3 measurements were confirmed at stationary reference monitors (see Supplemental Material Figure S6), with peaks above 80 ppb at Greeley, FTC-CSU, and FTC-West primarily to the west of the high O3 measured from the mobile laboratory. Overall there was a regional enhancement of O3on August 3 compared to July 23 levels, especially at the northern sites in the Front Range (Supplemental Material Figure S3).
Figure 9 Map of mobile laboratory O3 measurements on August 3, 2014. Map of O3 measured during the mobile laboratory drive on August 3. Times in red show the location of the mobile laboratory at that time and the black arrows indicate direction of travel. Five-hour back trajectories are shown by solid lines (blue arriving at 12:00 and red at 13:00) with hourly locations noted by outlined symbols. The portion of the drive circled in red was included in Figure 10. The southern portion of the drive took place after the gap in measurements. DOI: https://doi.org/10.1525/elementa.254.f9
Figure 10 shows that both methane and ethane levels were elevated above the values measured on the July 23 drive. The concurrent elevated ethane (25 to >35 ppb, much higher than on July 23rd) and methane is a marker for oil and gas emissions and shows that oil and gas O3 precursor emissions influenced the entire area sampled by the mobile laboratory. CO levels were the highest observed during the case study days in the morning, but decreased throughout the afternoon to ~160 ppb (slightly higher than the July 23 drive), indicating the presence of urban emissions that were less significant during the period of higher O3. The elevated CO levels from 11:15–12:15 were correlated with acetylene (not shown) with a coefficient of determination (R2) of 0.97, indicating an automobile source. From 12:15–13:00, the modestly elevated CO values during the higher O3 observations showed an oil and gas signature based on the correlation with ethane (R2 = 0.81) but not with acetylene. The ammonia and nitrous oxide values were higher than on July 23, implicating an agricultural methane source, but the elevated ethane during the drive confirms oil and gas as an additional source for the methane. Overall, the gas measurements display characteristics of oil and gas and agricultural sources with some additional urban source signatures. Therefore, the O3 production measured on the August 3 drive was likely due to a combination of local oil and gas sources as well as the transport of O3 or precursors from urban areas since agricultural methane sources are not a significant source of NMHCs.
Figure 10 Time series of gaseous species from mobile laboratory on August 3, 2014. Time series plots of gaseous species measured from a mobile laboratory during period of high O3 on August 3. Methane and ethane (a), CO and O3(b), methane and ammonia (c), and methane and nitrous oxide (d). DOI: https://doi.org/10.1525/elementa.254.f10
There was no precipitation measured at the Greeley airport on August 3 and the maximum temperature was 31°C. The sky was clear the entire day, and overall the weather was conducive to photochemical O3 production. During the time period of increasing O3 (11:15–13:00) the winds were mixed between the southwest, southeast, and west–northwest and the wind speeds were lower than those measured on July 23 (see the wind rose in Figure 11). Surface winds in the Greeley area during the early afternoon on August 3 were similar to Figure 11 and came mostly from the south and southeast (Weather Underground, 2014). The back trajectories (see Figure 9) demonstrated potential transport from urban Greeley of the air parcel arriving to the drive area at 12:00 (blue solid line), while the 13:00 trajectory (red solid line) was more stagnant and originated just southwest of the high O3drive measurements. Back trajectories on August 3 are in basic agreement with the surface winds measured by the mobile laboratory and displayed in Figure 11. Based on the surface winds and back trajectories, emissions measured throughout the drive included oil and gas and agricultural sources, in addition to urban emissions plumes from the Greeley area during the earlier portion of the drive.
Figure 11 Wind rose from mobile laboratory drive on August 3, 2014. Wind rose displaying mobile lab meteorological data during period of high O3 from 11:15–13:00 on August 3. Wind speed (in m/s) is indicated by color and the percentages represent the frequency of wind coming from a particular direction. DOI: https://doi.org/10.1525/elementa.254.f11
The flasks on August 3 (see Figure 12) were collected in Platteville, northwest of Denver (same site as July 23), and in the Rocky Mountains at two different sites (39.94, –105.58 and 40.38, –105.63). The Platteville flasks had significantly higher propane, ethane, benzene, n-butane, and n-pentane than any of the other flasks and they also demonstrated correlations that indicate that the source of the VOCs was oil and gas activities. The correlation coefficients on August 3 were similar to July 23, but overall the mixing ratios measured in the flasks were higher for all species on August 3 than on July 23. The flasks collected in Denver and in the Rocky Mountains all have lower light alkanes mixing ratios than in Platteville, and they also appear to be very similar to each other. This further enforces the pattern that the Platteville air was strongly influenced by local oil and gas operations emissions and those VOC emissions are not transported from Denver or the mountains to the west. The flask collected in the morning (at 8:30) had the highest oil and gas marker levels (upper right point in the plots in Figure 12), while the lowest levels at Platteville were measured in the afternoon at 14:30. The timing of the flask collection presents an explanation for why the surface O3 was not as high in Platteville (peak of 67 ppb) as it was in Greeley, FTC-CSU, or FTC-West (peaks of 84, 83, and 81 ppb, respectively), despite the presence of oil and gas emissions as presented in Figure 12. Oil and gas O3 precursor mixing ratios were decreasing in the Platteville area throughout the early afternoon (boundary layer growth), leading to lower peak O3 mixing ratios measured at that surface monitoring station. On August 3 isoprene mixing ratios in the flasks at Platteville were on average only 0.03 ppb, indicating limited potential for isoprene to dominate VOC reactivity and O3 formation.
Figure 12 Mixing ratios measured in flasks collected on August 3, 2014. Mixing ratios measured in flasks collected on August 3 displaying coefficients of determination of ethane vs. propane (a), benzene vs. propane (b), n-butane vs. propane (c), and n-pentane vs. propane (d). DOI: https://doi.org/10.1525/elementa.254.f12
August 13, 2014: Oil and gas emissions and localized elevated O3
On August 13 the drive lasted from 7:20 to 14:20 and was located northeast of Greeley (see Figure 13). The O3 measured during this drive was elevated ~20–30 ppb above median levels on limited production days and was approximately 75–80 ppb from 13:00–13:45. Of the surface sites, only Fort Collins–CSU recorded hourly O3 above 75 ppb (with a peak nearing 90 ppb). Platteville and Greeley both had moderate peaks of 70 ppb (Supplemental Material Figure S4), demonstrating that the high O3 measured on the drive was more localized to the northeast area and high O3 at the fixed monitoring sites was most prominent at the northernmost locations (Fort Collins).
Figure 13 Map of mobile laboratory O3 measurements on August 13, 2014. Map of O3 measured during the mobile laboratory drive on August 13. Times in red show the location of the mobile laboratory at that time and the black arrows indicate direction of travel. Five-hour back trajectories are shown by solid lines (blue arriving at 13:00 and red at 14:00) with hourly locations noted by outlined symbols. The portion of the drive circled in red was included in Figure 14. DOI: https://doi.org/10.1525/elementa.254.f13
As shown in Figure 14, the ethane levels recorded during the high O3 period were comparable to the measurements on the August 3, 2014 drive and remained between 30 and 40 ppb except for one dip around 13:45. These ethane levels are very high relative to the 1.29 ppb annual average regional background reported by Thompson et al. (2014). The methane level was also elevated above background levels, ranging from 2 to 2.2 ppb. The CO levels of ~150 ppb were well correlated with the ethane levels from 13:00–14:10 with a coefficient of determination (R2) of 0.87. CO did not show correlation with acetylene for the majority of the drive until a slight increase in CO was associated with acetylene (R2 = 0.93 from 13:55–14:10), suggesting that the dominant CO source through most of the drive was related to oil and gas extraction and processing activity as opposed to automobiles. CO and NOx are often co-emitted from inefficient combustion processes. Oil and gas related combustion activities that likely produced the measured enhanced CO would also produce adequate NOx for O3 production. Ammonia and nitrous oxide levels were on average much lower and did not show any of the large spikes seen on the August 3 drive, eliminating agricultural emissions as contributors to the observed methane levels.
Figure 14 Time series of gaseous species from mobile laboratory on August 13, 2014. Time series plots of gaseous species measured from a mobile laboratory during period of high O3 on August 13. Methane and ethane (a), CO and O3(b), methane and ammonia (c), and methane and nitrous oxide (d). DOI: https://doi.org/10.1525/elementa.254.f14
The weather on August 13 was clear skies all day with no precipitation and a maximum temperature of 32.8°C. These conditions were similar to August 3 and favorable for O3generation. The wind direction during the high O3 period on August 13 was variable, primarily out of the south, with low speed (see Figure 15). These winds were consistent with those measured at other sites throughout the Front Range; Platteville and BAO demonstrated predominantly southeast winds during the early afternoon and weather stations in Greeley and northeast of the drive location measured winds from the southwest and southeast from 10:00 AM to 1:00 PM (Weather Underground, 2014). These sites confirm that winds on August 13 did have significant southerly components that could carry O3 precursors to the northern portion of the gas field where the drive took place. Back trajectories shown in Figure 13 (blue and red solid lines) confirmed that air parcels reaching the drive route during the period of high O3 originated in a remote area of the gas field with oil and gas emission sources (wells) and agricultural sources (CAFOs), but did not pass through more urban areas. Although the winds are generally light, the dominant direction out of the south is consistent with the air parcel transport shown in the trajectories. The surface winds and trajectories, coupled with the low levels of ammonia and nitrous oxide measured during the drive, indicate that no O3 precursor emissions other than oil and gas related were observed.
Figure 15 Wind rose from mobile laboratory drive on August 13, 2014. Wind rose displaying mobile lab meteorological data during period of high O3 from 13:00–14:00 on August 13. Wind speed (in m/s) is indicated by color and the percentages represent the frequency of wind coming from a particular direction. DOI: https://doi.org/10.1525/elementa.254.f15
The flasks from August 13 were all located slightly north of Denver with the exception of one flask in Platteville. The Platteville flask had moderate mixing ratios of oil and gas tracers and an isoprene mixing ratio of 0.06 ppb. Overall, the flask data did not provide significant additional insight into pollution sources during the drive, but they did show that isoprene mixing ratios were not significantly higher on August 13 than on July 23 or August 3, demonstrating that isoprene was not likely to be the major source of VOCs for the high O3 measured during the drive.
The average mixing ratios of ethane, CO, and O3 that were measured by the mobile laboratory on the three case study drives are shown in Figure 16. While on July 23 and August 13 the O3 had likely reached the daily peak by the drive times shown in Figure 16(See Supplemental Material Figure S5 and Figure S7 – typically O3 reaches the daily peak by ~14:00), based on measurements at the nearby Greeley monitoring site, on August 3 it is likely that additional growth would have taken place in the vicinity of the mobile laboratory drive. The average O3 at Greeley from 13:00–14:00 was 81 ppb and that value is included in Figure 16 as an estimate for the peak value along the drive route. August 3 and 13 demonstrated higher average O3 and ethane than July 23 as measured by the mobile laboratory. August 3 showed more potential for urban emission influence on O3 production with the highest average CO levels of the three days, but the highly elevated ethane on August 3 shows definite presence of oil and gas O3 precursors on that day. August 13 demonstrates that in the NFR, high O3 can be produced in portions of the basin on a day when the dominant emission signature is oil and gas (indicated by ethane levels well above regional background) with low urban emissions (indicated by CO). The high O3 mixing ratios measured by the mobile laboratory on August 13 were not observed at the Greeley and Platteville surface monitors and the surface winds did not implicate transport of O3from other areas as the source of the high O3. These findings demonstrate that oil and gas activities were the primary source of O3 production of 20–30 ppb above median summertime levels observed northeast of Greeley on the drive on August 13, 2014.
Figure 16 Average ethane, CO, and O3 measured during the case study drives. Averages of one hour of measurements of ethane, CO, and O3 during the mobile laboratory drives on July 23, August 3, and August 13, 2014. The time of day of each drive that was included is shown in the legend. Average O3 at the Greeley monitoring site from 13:00–14:00 is included (gray cross hatched square) to estimate peak O3 in the drive vicinity on August 3. DOI: https://doi.org/10.1525/elementa.254.f16
Conclusions
Median summertime NFR O3 mixing ratio on days with limited photochemical production within the boundary layer was within the range of 45–55 ppb based on observations at Pawnee Buttes, a monitoring site that is minimally impacted by Front Range emissions. During the summer of 2014, O3 levels were lower on average than the summer of 2013, but there were still exceedances of the EPA NAAQS standard of 75 ppb (8-hour average) at multiple sites throughout the Front Range.
Three case study days were selected from the FRAPPE/DISCOVER-AQ study period to evaluate O3, emissions linked to O3 precursors, and meteorology, using extensive measurements collected throughout the Front Range by instrumented vehicles. These case studies form the basis for attributing oil and gas related emissions to significant O3enhancements seen on two of the drives while the third drive provides a baseline case with relatively low O3 enhancement and lower emissions with potential for O3 formation. Three dates of interest considered in this study were July 23, August 3, and August 13, 2014. The weather during these three afternoons was consistently sunny and warm and therefore conducive to photochemical O3 production.
July 23 represented a lower, background-level emissions day for the Greeley region where the mobile laboratory drive took place, with low mixing ratios of ethane, methane, CO, ammonia, and nitrous oxide relative to other days. O3 measurements on the drive were equal to median levels on low photochemical production days (~45–55 ppb), with a spike of 15 ppb of O3 enhancement as the mobile laboratory moved closer to the heart of oil and gas emissions. High O3 levels were measured at the Platteville monitoring site, as were high oil and gas NMHCs. O3 levels on August 3 were high at multiple monitoring stations, and the mobile laboratory drive measured O3 up to 30 ppb above median low photochemical production summertime levels concurrently with elevated ethane and methane, indicating the presence of oil and gas emissions. Based on back trajectories and correlations of CO with acetylene and ethane, the high O3 observed on August 3 was due primarily to oil and gas emissions with additional urban influence. The mobile laboratory drive on August 13 provided a strong case for examining the potential impact of oil and gas emissions and related activities on O3 production in the NFR of Colorado. The drive on August 13 measured high O3 levels (20–30 ppb above median low photochemical O3production days) in a remote area northeast of Greeley. High O3 levels were linked to elevated ethane and methane, similar to the August 3 case, indicating an oil and gas source of O3 precursors but with low mixing ratios of tracers of agricultural and urban emissions. Although wind data from the mobile laboratory platform suggested possible transport from both oil and gas and urban areas to the remote drive area on this day there was a clear absence of any measured urban pollution signature confirmed by the back trajectory analysis. While previous studies have focused on the overall enhancement of O3 due to oil and gas in the Front Range and found the impact to be between 6 and 11 ppb, these results demonstrate that on individual days, oil and gas can contribute locally up to 30 ppb to O3production.
The complex meteorology of the NFR in combination with the variety of emission sources, create a unique setting for high summertime O3 levels. The data presented in this paper indicate that high O3 is occurring in remote areas in the Wattenberg oil and gas field on days when oil and gas related emissions are the dominant source of precursors. Based on this work, more extensive measurements of O3, NOx, and NHMCs in the NFR are recommended to better understand spatial variability in O3 formation and quantify the magnitude of the contributions from each emission sector throughout the region. Better understanding of emissions and of the relative contributions of the various pollutants in the region will increase the effectiveness of mitigation actions and regulations. Since exceedances of EPA standards are based on single days and not overall enhancement averaged over multiple days, the high O3 mixing ratios measured on days when oil and gas activity is a primary contributor to elevated O3 levels make it imperative to better quantify O3 production contribution from oil and gas operations emissions to support strategies for staying within the NAAQS for O3 in the region.
Data Accessibility Statement
Data sources are cited in the text of the manuscript with the URLs listed in the references.
Supplemental Files
The supplemental files for this article can be found as follows:
- Figure S1. Comparison of mobile laboratory O3 measurements with Greeley monitoring site on August 8, 2014.
DOI: https://doi.org/10.1525/elementa.254.s1 - Figure S2. Comparison of mobile laboratory O3 measurements with Greeley monitoring site on August 3, 2014.
DOI: https://doi.org/10.1525/elementa.254.s1 - Figure S3. Comparison of mobile laboratory O3 measurements with Greeley monitoring site on August 5, 2014.
DOI: https://doi.org/10.1525/elementa.254.s1 - Figure S4. O3 measurements at six surface sites for a week in July 2014.
DOI: https://doi.org/10.1525/elementa.254.s1 - Figure S5. O3 measurements at six surface sites on July 23, 2014.
DOI: https://doi.org/10.1525/elementa.254.s1 - Figure S6. O3 measurements at six surface sites on August 3, 2014.
DOI: https://doi.org/10.1525/elementa.254.s1 - Figure S7. O3 measurements at six surface sites on August 13, 2014.
DOI: https://doi.org/10.1525/elementa.254.s1
Acknowledgements
Meteorological data for BAO Tower were provided by Daniel Wolfe of the NOAA ESRL Physical Sciences Division. The authors thank Eric Williams and the NOAA Chemical Sciences Division for providing surface O3 data in Platteville during June, July, and August of 2013 and 2014. Surface O3 and meteorological data in Platteville during July and August of 2014 were collected by AMT and the Pennsylvania State University NATIVE mobile laboratory. Special thanks to Hannah Halliday (Penn State) for trailer operations and for collecting the UCI flask samples at Platteville. Thank you to Cody Floerchinger and Aerodyne Research for providing mobile laboratory data. Thank you to CDPHE for providing surface O3 data. The authors gratefully acknowledge the NOAA Air Resources Laboratory (ARL) for the provision of the HYSPLIT transport and dispersion model used in this publication. The reviewers of the manuscript provided detailed and insightful comments that significantly improved the manuscript.
Funding information
Study was funded by and provided in-kind support by:
- National Oceanic and Atmospheric Administration (NOAA) Climate Program Office (LCC, SJO, RCS, AM-B)
- AirWaterGas Sustainability Research Network funded by the National Science Foundation (NSF) under Grant No. CBET-1240584 (GP)
- Regional Air Council, Denver, Colorado (DRB)
- Colorado Department of Public Health and Environment (CDPHE) receives funding from the United States Environmental Protection Agency and other Colorado state grants (EJM)
- Sensor Service America, Inc. (SSAI) and NASA, Grant Number NNL11AA00B, Task Order A-082G CY3 (SCH)
- Pennsylvania State University, Grant Number NNX10AR39G (AMT)
Competing interests
The authors have no competing interests to declare.
Author Contributions
- Contributed to conception and design: LCC, SJO, GP
- Contributed to acquisition of data: EJM, SCH, DB, AMT, AM-B
- Contributed to analysis and interpretation of data: LCC, SJO, GP, RCS, EJM, DRB, AMT
- Drafted and/or revised the article: LCC, SJO, GP, RCS
- Approved the submitted version for publication: LCC, SJO, GP, RCS, EJM, SCH, DRB, AMT, AM-B
References
- Abeleira, A, Pollack, IB, Sive, B, Zhou, Y, Fischer, EV, et al. 2017 Source characterization of volatile organic compounds in the Colorado Northern Front Range Metropolitan Area during spring and summer 2015. J Geophys Res Atmos 122(6): 3595–3613. DOI: 10.1002/2016JD026227
- Baker, AK, Beyersdorf, AJ, Doezema, LA, Katzenstein, A, Meinardi, S, et al. 2008 Measurements of nonmethane hydrocarbons in 28 United States cities. Atmos Enviro 42(1): 170–182. DOI: 10.1016/j.atmosenv.2007.09.007
- Code of Colorado Regulations 2016 5 CCR 1001–9 Regulation Number 7. Available at: https://www.sos.state.co.us/CCR/DisplayRule.do?action=ruleinfo&ruleId=2341&deptID=16&agencyID=7&deptName=Department%20of%20Public%20Health%20and%20Environment&agencyName=Air%20Quality%20Control%20Commission&seriesNum=5%20CCR%201001-9.
- Colman, JJ, Swanson, AL, Meinardi, S, Sive, BC, Blake, DR, et al. 2001 Description of the analysis of a wide range of volatile organic compounds in whole air samples collected during PEM-tropics A and B. Anal Chem 73(15): 3723–3731. DOI: 10.1021/ac010027g
- Colorado Department of Public Health and the Environment (CDPHE) 2015 EPA lowers federal ozone standard; Colorado, other states face more difficult compliance. Denver, Colorado: Colorado Department of Public Health and the Environment.
- Colorado Department of Public Health and the Environment (CDPHE) 2016 2015 Air Quality Data Report. Denver, Colorado: Colorado Department of Public Health and the Environment. APCD-TS-B1.
- Colorado Oil and Gas Conservation Commission (COGCC) 2014a County level production data available in the “Data: COGIS DataBase” section [dataset]. Available at: http://cogcc.state.co.us/.
- Colorado Oil and Gas Conservation Commission (COGCC) 2014b 2014 Production Summary available in the “Data: Downloads” section [dataset]. Available at: http://cogcc.state.co.us/.
- Czepiel, PM, Moscher, B, Harriss, RC, Shorter, JH, McManus, JB, et al. 1996 Landfill methane emissions measured by enclosure and atmospheric tracer methods. J Geophys Res Atmos 101(D11): 16711–16719. DOI: 10.1029/96JD00864
- Edwards, PM, Brown, SS, Roberts, JM, Ahmadov, R, Banta, RM, et al. 2014 High winter ozone pollution from carbonyl photolysis in an oil and gas basin. Nature 514(7522): 351–354. DOI: 10.1038/nature/13767
- Eisele, A, Hannigan, M, Milford, J, Helmig, D, Milmoe, P, et al. 2009 Understanding Air Toxics and Carbonyl Pollutant Sources in Boulder County, Colorado. EPA Technical Report , 141.
- Energy Information Administration (EIA) 2015 Natural Gas Annual 2014. Washington, D.C.: U.S. Dept. of Energy.
- Evans, JM and Helmig, D 2016 Investigation of the influence of transport from oil and natural gas regions on elevated ozone levels in the northern Colorado front range. J Air Waste Manag Assoc 67(2): 196–211. DOI: 10.1080/10962247.2016.1226989
- Field, RA, Soltis, JJ, Pérez-Ballesta, P, Grandesso, E and Montague, DC 2015 Distributions of air pollutants associated with oil and natural gas development measured in the Upper Green River Basin of Wyoming. Elem Sci Anth 3(74). DOI: 10.12952/journal.elementa.000074
- Fiore, AM, West, JJ, Horowitz, LW, Naik, V and Schwarzkopf, MD 2008 Characterizing the tropospheric ozone response to methane emission controls and the benefits to climate and air quality. J Geophys Res 113(D8): 2156–2202. DOI: 10.1029/2007JD009162
- Fortin, TJ, Howard, BJ, Parrish, DD, Goldan, PD, Kuster, WC, et al. 2005 Temporal changes in U.S. benzene emissions inferred from atmospheric measurements. Environ Sci Technol 39(6): 1403–1408. DOI: 10.1021/es049316n
- Galbally, IE, Schultz, MG, Buchmann, B, Gilge, S, Guenther, F, et al. 2013 Guidelines for Continuous Measurements of Ozone in the Troposphere. Geneva, Switzerland: World Meteorological Organization. WMO-No. 1110.
- Gilman, JB, Lerner, BM, Kuster, WC and de Gouw, JA 2013 Source signature of volatile organic compounds from oil and natural gas operations in northeastern Colorado. Environ Sci Technol 47(3): 1297–1305. DOI: 10.1021/es304119a
- Gupta, D and Singh, SK 2012 Greenhouse gas emissions from wastewater treatment plants: A case study of Noida. J Water Sustain 2(2): 131–139.
- Halliday, HS, Thompson, AM, Wisthaler, A, Blake, DR, Hornbrook, RS, et al. 2016 Atmospheric benzene observations from oil and gas production in the Denver-Julesburg Basin in July and August 2014. J Geophys Res 121(18): 11055–11074. DOI: 10.1002/2016JD025327
- Helmig, D, Rossabi, S, Hueber, J, Tans, P and Montzka, SA, et al. 2016 Reversal of global atmospheric ethane and propane trends largely due to US oil and natural gas production. Nat Geosci 9: 490–495. DOI: 10.1038/ngeo2721
- Helmig, D, Thompson, CR, Evans, J, Boylan, P, Hueber, J, et al. 2014 Highly elevated atmospheric levels of volatile organic compounds in the Uintah Basin, Utah. Environ Sci Technol 48(9): 4707–4715. DOI: 10.1021/es405046r
- Herndon, SC, Jayne, JT, Zahniser, MS, Worsnop, DR, Knighton, B, et al. 2005 Characterization of urban pollutant emission fluxes and ambient concentration distributions using a mobile laboratory with rapid response instrumentation. Farad Discuss 130: 327–339. DOI: 10.1039/b500411j
- Losleben, M, Pepin, N and Moore, S 2000 Air Flow over Complex Terrain in the Colorado Front Range. Boulder, Colorado: Institute of Arctic and Alpine Research: University of Colorado. Available at: http://culter.colorado.edu/Climate/Mrsclimate/agu2000.pdf.
- McDuffie, EE, Edwards, PM, Gilman, JB, Lerner, BM, Dubé, WP, et al. 2016 Influence of oil and gas emissions on summertime ozone in the Colorado Northern Front Range. J Geophys Res 121(14): 8712–9729. DOI: 10.1002/2016JD025265
- McManus, JB, Zahniser, MS, Nelson, DD, Shorter, JH, Herndon, SC, et al. 2015 Recent progress in laser-based trace gas instruments: performance and noise analysis. Appl Phys B 119(1): 203–218. DOI: 10.1007/s00340-015-6033-0
- National Aeronautics and Space Administration (NASA) 2015 Colorado 2014 Data Archive: DISCOVER-AQ [dataset]. Available at: https://www-air.larc.nasa.gov/missions/discover-aq/discover-aq.html.
- National Oceanic and Atmospheric Administration Chemical Sciences Division (NOAA CSD) 2014 2011–2014 Platteville O3 Data [dataset]. Available at: https://www.esrl.noaa.gov/csd/groups/csd7/measurements/2011platteville/datadownload/o3Data1MinAvg.php.
- National Weather Service (NWS) 2015 NWS Boulder Denver Top 20 Records – July [dataset]. Silver Spring, Maryland: National Weather Service. Available at: https://www.weather.gov/bou/den_extreme_jul.
- Oltmans, S, Schnell, R, Johnson, B, Pétron, G, Mefford, T, et al. 2014 Anatomy of wintertime ozone production associated with oil and gas extraction activity in Wyoming and Utah. Elementa 2. DOI: 10.12952/journal.elementa.000024
- Oltmans, SJ, Karion, A, Schnell, RC, Pétron, G, Helmig, D, et al. 2016 O3, CH4, CO2, CO, NO2, and NMHC aircraft measurements in the Uinta Basin oil and gas region under low and high ozone conditions in winter 2012 and 2013. Elem Sci Anth 4. DOI: 10.12952/journal.elementa.000132
- Parrish, DD 2006 Critical evaluation of US on-road vehicle emission inventories. Atmos Environ 40(13): 2288–2300. DOI: 10.1016/j.atmosenv.2005.11.033
- Pétron, G, Frost, G, Miller, BR, Hirsch, AI, Montzka, SA, et al. 2012 Hydrocarbon emissions characterization in the Colorado Front Range: A pilot study. J Geophys Res 117(D4): 2156–2202. DOI: 10.1029/2011JD016360
- Pétron, G, Karion, A, Sweeney, C, Miller, BR, Montzka, SA, et al. 2014 A new look at methane and nonmethane hydrocarbon emissions from oil and natural gas operations in the Colorado Denver-Julesburg Basin. J Geophys Res 119(11): 6836–6852. DOI: 10.1002/2013JD021272
- Rappenglück, B, Ackermann, L, Alvarez, S, Golovko, J, Buhr, M, et al. 2014 Strong wintertime ozone events in the Upper Green River, Wyoming. Atmos Chem Phys 14: 4909–4934. DOI: 10.5194/acp-14-4909-2014
- Reddy, PJ 2008 Ozone Photochemistry and Meteorology: Presentation for the Regional Air Quality Council Ozone Stakeholders Meeting. Available at: http://ozoneaware.org/postfiles/documentsandpresentations/oil_gas/Ozone%20Briefing.pdf.
- Rolph, G, Stein, A and Stunder, B 2017 Real-time Environmental Applications and Display sYstem: READY. Environ Model Softw 95: 210–228. DOI: 10.1016/j.envsoft.2017.06.025
- Schnell, RC, Johnson, BJ, Oltmans, SJ, Cullis, P, Sterling, C, et al. 2016 Quantifying wintertime boundary layer ozone production from frequent profile measurements in the Uinta Basin, UT oil and gas region. J Geophys Res Atmos 121(18): 11038–11054. DOI: 10.1002/2016JD025130
- Schnell, RC, Oltmans, SJ, Neely, RR, Endres, MS, Molenar, JV, et al. 2009 Rapid photochemical production of ozone at high concentrations in a rural site during winter. Nat Geosci 2: 120–122. DOI: 10.1038/ngeo415
- Stein, AF, Draxler, RR, Rolph, GD, Stunder, BJB, Cohen, MD, et al. 2015 NOAA’s HYSPLIT atmospheric transport and dispersion modeling system. Bull Ameri Meteor Soc 96: 2059–2077. DOI: 10.1175/BAMS-D-14-00110.1
- Swarthout, RF, Russo, RS, Zhou, Y, Hart, AH and Sive, BC 2013 Volatile organic compound distributions during the NACHTT campaign at the Boulder Atmospheric Observatory: Influence of urban and natural gas sources. J Geophys Res 118: 10614–10637. DOI: 10.1002/jgrd.50722
- Té, Y, Dieudonné, E, Jeseck, P, Hase, F, Hadji-Lazaro, J, et al. 2012 Carbon monoxide urban emission monitoring: A ground-based FTIR case study. J Atmos Oceanic Tech 29: 911–921. DOI: 10.1175/JTECH-D-11-00040.1
- Thompson, CR, Hueber, J and Helmig, D 2014 Influence of oil and gas emissions on ambient atmospheric non-methane hydrocarbons in residential areas of Northeastern Colorado. Elem Sci Anth 2014; 3: 35. DOI: 10.12952/journal.elementa.000035
- Toth, JJ and Johnson, RH 1985 Summer surface flow characteristics over northeast Colorado. Mon Weather Rev 113(9): 1458–1469. DOI: 10.1175/1520-0493(1985)113<1458:SSFCON>2.0.CO;2
- United States Environmental Protection Agency (U.S. EPA) 2011 Data from the 2011 National Emissions Inventory [dataset]. Available at: https://www.epa.gov/air-emissions-inventories/2011-national-emissions-inventory-nei-data.
- United States Environmental Protection Agency (U.S. EPA) 2013a 2013 Final Report: Integrated Science Assessment of Ozone and Related Photochemical Oxidants. Washington, D.C.: U.S. Environmental Protection Agency. EPA/600/R-10/076F.
- United States Environmental Protection Agency (U.S. EPA) 2013b Transfer Standards for Calibration of Air Monitoring Analyzers for Ozone. Research Triangle Park, North Carolina: U.S. Environmental Protection Agency. EPA-454/B-13-004.
- United States Environmental Protection Agency (U.S. EPA) 2014 Data from the 2014 National Emissions Inventory [dataset]. Available at: https://www.epa.gov/air-emissions-inventories/2014-national-emissions-inventory-nei-data/.
- United States Environmental Protection Agency (U.S. EPA) 2015 Noble Energy, Inc. Settlement. Available at: https://www.epa.gov/enforcement/noble-energy-inc-settlement.
- Watson, JG, Fujita, E, Chow, JC and Zielinska, B 1998 Northern Front Range Air Quality Study Final Report. Reno, Nevada: Desert Research Institute. DRI Document No. 6580-685-8750.1F2.
- Weather Underground 2014 Weather History for KGXY [dataset]. Atlanta, Georgia: The Weather Company, LLC. Available at: https://www.wunderground.com/history/airport/KGXY/.
- Whitby, RA and Altwicker, ER 1978 Acetylene in the atmosphere: Sources, representative ambient concentrations, and ratios to other hydrocarbons. Atmos Environ 12(6–7): 1289–1296. DOI: 10.1016/0004-6981(78)90067-7
- Xiao, Y, Logan, JA, Jacob, DJ, Hudman, RC, Yantosca, R, et al. 2008 Global budget of ethane and regional constraints on U.S. sources. J Geophys Res 113(D21306). DOI: 10.1029/2007JD009415
- Yacovitch, TI and Herndon, SC 2014 TILDAS Ethane Quality Assurance Document Discover AQ Denver 2014. Available at: https://www-air.larc.nasa.gov/cgi-bin/enzFile?c1684C5749C53F525A470B4E964F4D05ABA2f7075622d6169722f444953434f56455241512f434f4c4f5241444f5f323031342f5033425f41495243524146542f5941434f56495443482e544152412f646973636f76657261712d433248365f5033425f5175616c6974794173737572616e6365446f63756d656e745f52312e646f6378.
- Yacovitch, TI, Herndon, SC, Roscioli, JR, Floerchinger, C, McGovern, RM, et al. 2014 Demonstration of an ethane spectrometer for methane source identification. Environ Sci Technol 48(14): 8028–8034. DOI: 10.1021/es501475q
Weld, other Front Range counties failing to meet EPA ozone standards; tougher regulations potentially in store, by Joe Moylan, Greeley Tribune, November 9, 2018
The Colorado Regional Air Quality Council in Denver announced last week a nine-county region along the Front Range failed to meet U.S. Environmental Protection Agency ozone standard, meaning stricter regulations may be in Colorado’s future.
The nine-county region includes Weld, Larimer, Denver, Boulder, Adams, Douglas, Jefferson, Arapahoe and Broomfield counties. The EPA had given the Denver metro and northern Front Range regions until July 20 to bring ozone levels to within 75 parts per billion, a standard set in 2008.
The 75 parts per billion requirement is averaged over an eight-hour span. The region isn’t yet required to meet the most recent ozone standard 70 parts per billion, which was set in 2015.
There are two types of ozone. The “good” ozone layer can be found in Earth’s stratosphere, which extends upward from about six to 30 miles above the surface and protects life from the sun’s harmful ultraviolet rays.
Ground level, or “bad” ozone, is not emitted directly into the air, but rather created by chemical reactions between oxides of nitrogen and volatile organic compounds in the presence of sunlight. Emissions from industrial facilities and electric utilities, motor vehicle exhaust, gasoline vapors, and chemical solvents are some of the major sources of NOx and VOCs, according to the EPA.
HOW TO REDUCE OZONE-CAUSING EMISSIONS
- Make sure vehicle is maintained.
- Comply with emissions program.
- Drive less, use public transportation, walk and ride a bike.
- Maintain lawn mower or go electric.
- Stop at the “click” when filling up at gas tank.
- Mow after 5 p.m. to help prevent emissions from reacting with sunlight and turning into bad ozone.
Since the Front Range failed to meet the 75 parts per billion mark, the EPA is expected to downgrade the region to “serious nonattainment” from “moderate nonattainment.” The nine-county Front Range region is one of 37 regions throughout the country not yet up to snuff on 2008 ozone standards. Those 37 regions encompass 160 counties and are home to more than 104 million Americans, according to the EPA Green Book.
The downgrade means stricter regulations are in Colorado’s future, said Mike Silverstein, executive director of the Regional Air Quality Council in Denver. Appointed by the governor, members of the Regional Air Quality Council are tasked with coming up with recommendations to reduce ozone-causing emissions. It works “hand-in-glove” with the Colorado Department of Public Health and Environment, which puts those recommendations into practice and enforces them.
The Regional Air Quality Council is taking an across-the-board approach in terms of how to reduce ozone. In the past, it has installed electric vehicle charging stations throughout the region and will continue to do so in the future. It also has spent a lot of money converting diesel- and gasoline-burning garbage trucks and buses to cleaner burning fuels, such as natural gas. It’s also looking at whether or not to adopt stricter standards on paint and cleaning products, which also emit NOx and VOCs into the atmosphere.
Then there are the two primary sources of ozone-causing emissions — the oil and gas industry, and fossil-fuel burning vehicles.
“The oil and gas industry is the largest source category of emissions, mainly because it’s such a large industry with such a big footprint,” Silverstein said. “Just like a lot of vehicles together makes up the second biggest source category.”
Although it’s too soon to know what new regulations might be coming down the pipeline, reducing bad ozone in the region is challenging because of factors outside of anyone’s control. For example, Colorado inherits a negligible amount of its bad ozone — somewhere between one and five parts per billion — from pollution traveling here from China and California.
Weather, or more accurately a lack thereof, also plays a big role in the region’s air quality problems.
HEALTH PROBLEMS FROM OZONE
- Difficult to breathe deeply and vigorously.
- Shortness of breath, and pain when taking a deep breath.
- Coughing and sore or scratchy throat.
- Inflame and damage the airways.
- Aggravate lung diseases such as asthma, emphysema and chronic bronchitis.
- Increase the frequency of asthma attacks.
- Make the lungs more susceptible to infection.
- Continue to damage the lungs even when the symptoms have disappeared.
- Cause chronic obstructive pulmonary disease.
Cindy Shellito, professor of meteorology at the University of Northern Colorado, said during the summer high-pressure systems move into the area and can linger over the region for extended periods of time. High-pressure systems result in stagnant air, which causes those dry, warm and sunny days for which the Front Range is famous.
It’s not uncommon for a high-pressure system to roll into the area and last all summer, Shellito said. But stagnant air also means nothing is moving. The bad ozone stays in the area because there is no weather to push it out.
“Sometimes that could mean waiting on a monsoonal pattern to come into the area, which doesn’t generally happen until late July or August,” Shellito said. “If you don’t have weather, the bad ozone just sits in the region.”
It’s no coincidence the nine-county Front Range region experienced at least 23 ozone action days from late June to early September. But Shellito said managing bad ozone isn’t just a summer time problem.
In the winter, cold air can “dam up” against the Rocky Mountains, which also traps ozone in the region until a new weather system moves into the area and pushes it out. Ozone levels can sometimes be just as high as during the summer because there are not only emissions from industries and vehicles, but also from people heating their homes.
Bringing the Front Range into 2008 EPA air quality compliance is a tall order. In order to monitor air quality, the Regional Air Quality Council and the Department of Public Health and Environment rely on data collected from 31 stations located throughout the state.
Fifteen of those stations are located along the Front Range, including one in Greeley at Weld Tower, on the north side of town not far from Island Grove Regional Park. Local levels on the worst air quality days ranged between 73 and 77 parts per billion.
The stations reporting the highest concentrations of ozone were typically closer to the foothills, including Rocky Flats, Chatfield State Park, west Fort Collins and the National Renewable Energy Laboratory near Golden. During the worst air quality days, those stations reported ozone concentrations close to 90 parts per billion, which doesn’t meet 1997 EPA standards, let alone 2008 and 2015 requirement.
— Joe Moylan covers crime and public safety for The Greeley Tribune. Reach him at jmoylan@greeleytribune.com, (970) 392-4467 or on Twitter @JoeMoylan.
Influence of oil and gas emissions on summertime ozone in the Colorado Northern Front Range
Abstract
Tropospheric O3 has been decreasing across much of the eastern U.S. but has remained steady or even increased in some western regions. Recent increases in VOC and NOxemissions associated with the production of oil and natural gas (O&NG) may contribute to this trend in some areas. The Northern Front Range of Colorado has regularly exceeded O3 air quality standards during summertime in recent years. This region has VOC emissions from a rapidly developing O&NG basin and low concentrations of biogenic VOC in close proximity to urban‐Denver NOx emissions. Here VOC OH reactivity (OHR), O3 production efficiency (OPE), and an observationally constrained box model are used to quantify the influence of O&NG emissions on regional summertime O3production. Analyses are based on measurements acquired over two summers at a central location within the Northern Front Range that lies between major regional O&NG and urban emission sectors. Observational analyses suggest that mixing obscures any OPE differences in air primarily influenced by O&NG or urban emission sector. The box model confirms relatively modest OPE differences that are within the uncertainties of the field observations. Box model results also indicate that maximum O3 at the measurement location is sensitive to changes in NOx mixing ratio but also responsive to O&NG VOC reductions. Combined, these analyses show that O&NG alkanes contribute over 80% to the observed carbon mixing ratio, roughly 50% to the regional VOC OHR, and approximately 20% to regional photochemical O3 production.
1 Introduction
Tropospheric ozone (O3) is a secondary pollutant that contributes to the degradation of regional air quality. The only known sources of tropospheric O3 are through the intrusion of O3‐rich stratospheric air [Roelofs and Lelieveld, 1995] and the oxidation of volatile organic compounds (VOCs) in the presence of nitrogen oxides (NOx = NO + NO2) [Chameides, 1978; Crutzen, 1970]. In the past two decades, summertime maximum O3 at rural (receptor) sites across much of the U.S. has exhibited a strongly decreasing trend [Cooper et al., 2012], likely in response to concurrent, declining NOx emissions [e.g., Butler et al., 2011; Cooper et al., 2012; Environmental Protection Agency, 2016]. Decreasing O3 trends have been most pronounced in the eastern U.S., but generally more moderate, or even increasing, at high‐elevation western sites [Cooper et al., 2012]. Proposed explanations for upward trends include increases in summer temperatures, contributions from stratospheric intrusions, long‐range transport of emissions from Asia, western wildfire activity, and/or regional oil and natural gas (O&NG) emissions [Cooper et al., 2012, 2015]. Here we focus on summertime O3 production impacted by O&NG activity in the Colorado Northern Front Range (NFR) (Figure 1), a region out of compliance with National Ambient Air Quality Standards (NAAQS) of 75 ppbv for O3 since 2007 and expected to remain so under recently revised 2015 standards of 70 ppbv [Colorado Department of Public Health and Environment (CDPHE), 2016].
The NFR (Figure 1) has urban O3 precursor emissions in close proximity to those from other sectors, principally agriculture (e.g., animal feedlots) and O&NG production. The NFR’s Wattenberg Gas Field of the greater Denver‐Julesburg Basin has seen significant recent increases in O&NG production, with the number of active wells nearly doubling in Weld County between January 2008 and July 2015 to over 27,000 [Colorado Oil and Gas Conservation Commission (COGCC), 2/2016]. Though O&NG production has increased in multiple U.S. basins, a relatively small number of these basins lie in close proximity to large urban areas, as is the case with the Denver‐Julesburg. Biogenic VOC mixing ratios (e.g., isoprene) are relatively low in the NFR compared to other U.S. O&NG producing regions, such as Texas and Pennsylvania [Rutter et al., 2015; Swarthout et al., 2015]. Lower biogenic mixing ratios may magnify the influence of O&NG emissions on regional O3 production. Multiple studies have extensively characterized NFR VOC emissions, including those from O&NG activity [e.g., Brantley et al., 2015; Gilman et al., 2013; Pétron et al., 2012, 2014; Swarthout et al., 2013], but remain limited in terms of characterizing their influence on summertime O3 production.
To date, relatively few studies have specifically assessed the influence of emissions associated with O&NG activity on regional summertime O3 production. Several recent papers have focused on wintertime O3 in O&NG producing regions in both the Upper Green River Basin of Wyoming [Field et al., 2015; Oltmans et al., 2014; Rappenglück et al., 2014; Schnell et al., 2009] and the Uintah Basin in Utah [Ahmadov et al., 2015; Carter and Seinfeld, 2012; Edwards et al., 2014, 2013; Helmig et al., 2014; Oltmans et al., 2014]. Winter O3, however, is distinct from summertime urban‐influenced O3 and has so far only occurred in remote areas with low population densities and urban emissions. Winter O3 is also specific to highly stable inversion conditions that cause an accumulation of VOC emissions from O&NG activity. The influence of O&NG emissions on summer O3 near urban areas is not well characterized and is a potentially complex issue arising from the interaction of a variety of emissions.
Previous summertime O3 analyses include two initial studies that used regional models to determine that O3 production was positively influenced by emissions associated with O&NG activity in the Haynesville region in Texas [Kemball‐Cook et al., 2010] and across multiple western U.S. locations [Rodriguez et al., 2009]. More recent work has suggested that O&NG‐associated NOx emissions, relative to those of VOCs, contribute disproportionally to summertime O3 production. For example, O&NG‐associated VOC emissions only contribute 8% to O3 precursors in California’s San Joaquin Valley [Gentner et al., 2014] and less than 20% and 7%, respectively, to the O3 forming potential in the Barnett Basin near Fort Worth, Texas [Rutter et al., 2015] and Pennsylvania’s Marcellus Basin [Swarthout et al., 2015]. Similarly, regional modeling of the Eagle Ford Basin in Texas showed that changes in regional summertime O3 concentrations were not driven by O&NG‐associated VOCs but rather by emissions of NOx [Pacsi et al., 2015]. As observed from space, NOx levels associated with O&NG activity (e.g., flaring and combustion from O&NG extraction machinery and transport vehicles) have recently increased over three O&NG producing regions in the central U.S. [Duncan et al., 2016]. In other states such as Pennsylvania, the influence of O&NG activity on O3 may be underestimated or obscured due to (1) NOx trends masked by surrounding urban emission reductions [Duncan et al., 2016] and/or (2) gaps in the monitoring network for Environmental Protection Agency (EPA) criteria pollutants, such as NO2 [Carlton et al., 2014].
Here we apply three methods to characterize the influence of VOC and/or NOx emissions on O3 production in the NFR. These include VOC OH reactivity (OHR), O3 production efficiency (OPE), and photochemical box modeling. The VOC OH reactivity (OHR) [e.g., Gilman et al., 2013] is a measure of the kinetic oxidation of VOCs by the OH radical and is often the rate limiting step in photochemical O3 production. A number of O&NG‐focused studies have used this metric to highlight the potential contribution of O&NG VOCs to O3 production in both summer and winter months [Field et al., 2015; Gilman et al., 2013; Rutter et al., 2015; Swarthout et al., 2013, 2015]. Although VOC OHR provides a simple assessment of the relative contribution of different VOCs to potential O3 production, it does not incorporate information about radical propagation or its NOx dependence, both of which are important for predicting the efficiency of O3 production. Ozone production efficiency (OPE) [e.g., Trainer et al., 1993] is a measure of the number of O3 molecules produced, or number of NOxinterconversion cycles completed, before NOx is lost through termination reactions (e.g., nitric acid (HNO3) or organic nitrate production). The OPE is defined as the slope of odd oxygen (Ox = NO2 + O3) plotted against NOz (NOz = NOy −NOx, where NOy is total oxidized reactive nitrogen). OPE analyses have been used to characterize urban and rural regions across the U.S. as documented in Table 1 of Griffin et al. [2004], but to our knowledge, have not been applied specifically to O3 production in an O&NG basin. The principle advantage to OPE is that it is an observable quantity that should differentiate between air parcels of different VOC composition and NOx mixing ratios, for example, those influenced by O&NG versus urban emissions. However, OPE derived from field observations is an upper limit as it suffers from artifacts such as depositional NOy loss.
Box model analyses are a common tool used to assess the sensitivity of O3 production to NOx and VOC emissions within air parcels of known composition. They have been used recently to model O3 production in western U.S. O&NG basins during winter months [Carter and Seinfeld, 2012; Edwards et al., 2013, 2014]. To our knowledge, a box model analysis has not been previously reported for summertime O3 production in an O&NG basin. Box models have the advantage of a fully explicit chemical mechanism, but they parameterize transport as a highly simplified, single dilution term. They therefore do not represent heterogeneity in the spatial distribution of emissions. They also do not rely on emission inventories, which can be an important source of uncertainty in three‐dimensional chemical transport models [e.g., Ahmadov et al., 2015], but parameterize emissions so as to match observations or constrain primary species to observed values. Box model analyses are useful in assessing the NOx and VOC sensitivities of O3 and other secondary products (e.g., acetone, MEK, and RONO2) for averaged data, in which chemical and meteorological variabilities average to typical values [Edwards et al., 2013], or in simulations of air parcel evolution along a known trajectory [Washenfelder et al., 2011b]. In these cases, box models provide a simple alternative to 3‐D chemical transport models.
We present a combination of VOC OHR and OPE analyses along with an observationally constrained box model to (1) quantify the impacts of O&NG emissions on summertime maximum O3 and its production efficiency at a specific location within the NFR and (2) evaluate the O3 sensitivity to NOx and VOC emissions. This analysis indicates that the influence of O&NG VOCs on regionally produced O3 is small relative to their contribution to total VOC mass and OHR, but not negligible on the scale relevant to attainment of regional air quality standards.
2 Experimental and Analysis Methods
2.1 Measurement Site
The Boulder Atmospheric Observatory (BAO; 40.05°N, 105.01°W, 1584 m above sea level) [Kaimal and Gaynor, 1983] lies roughly 35 km north of Denver and 25 km east of Boulder in the southwest corner of the Wattenberg Gas Field (Figure 1). The site has a tall (300 m) tower with south facing stationary platforms (booms) at 10, 100, and 300 m for meteorological measurements of temperature, relative humidity, wind speed, and direction. An external carriage mounted on the southwest side of the tower provides a platform for vertically resolved chemical measurements as described further in section S1 in the supporting information.
2.2 Field Campaigns
Measurements at BAO were made in July–August 2012 and July–August 2014, months when the NFR experiences O3 levels in exceedance of the EPA 8 h O3 standard [Colorado Department of Public Health and Environment (CDPHE), 2015]. During these two summers, the NFR was studied by three major field campaigns that contributed data to this analysis. Campaign and measurement descriptions can be found below and as a complete list in Table S1 in the supporting information.
2.2.1 SONNE: 2012
The Summer Ozone Near Natural gas Emissions (SONNE) field campaign was conducted at BAO between 27 July and 12 August 2012. Chemical measurements were acquired via inlets mounted 8 m above ground level (agl) on a walkup tower ~10 m south of the main tower. Continuous in situ measurements of a full suite of C2‐C10 hydrocarbons, C2‐C4 oxygenated VOCs, aromatics, C2‐C3 alkyl nitrates, and dimethyl sulfide were collected via a custom‐built, two‐channel gas chromatograph‐mass spectrometer (GC‐MS) [Gilman et al., 2010]. Samples were acquired (5 min) and analyzed (25 min) on a repeating cycle every 30 min. The accuracy and detection limits are compound‐dependent but less than 25% and 10 parts per trillion by volume (pptv), respectively [Gilman et al., 2010]. NOx and NO2 were measured with a custom‐built, multichannel cavity ring‐down (CRD) instrument. NO2 was measured by direct absorption at 405 nm, while NOx was simultaneously measured in a second channel after conversion of ambient NO to NO2 via an addition of excess O3 [Fuchs et al., 2009]. The accuracy and limit of detection for both species were <5% and <30 pptv, respectively. O3 was measured via UV absorbance by a commercial instrument (Thermo Environmental Instruments, Inc., Model 49c). Methane (CH4) was measured via CRD spectroscopy using a wavelength‐scanned CRD instrument (Picarro, model 1301 m) [Peischl et al., 2012]. Carbon monoxide (CO) was measured by a vacuum ultraviolet fluorescence instrument [Gerbig et al., 1999]. All chemical measurements were collected at a 1 Hz time resolution and averaged to the GC‐MS acquisition period of 5 min every half hour.
2.2.2 FRAPPÉ/DISCOVER‐AQ: 2014
In July–August 2014 the NSF Front Range Air Pollution and Photochemistry Éxperiment (FRAPPÉ) and the NASA Deriving Information on Surface Conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER‐AQ) field campaigns conducted aircraft, mobile, and ground‐based measurements at over 15 locations across the Front Range. Measurements at BAO took place between 16 July and 15 August 2014 and included surface and vertically resolved observations. CH4 and CO were measured from the instrument carriage with a commercial CRD instrument (Picarro, model 2401) [Chen et al., 2010, 2013; Crosson, 2008]. Gas‐phase ammonia (NH3) was measured from the carriage via infrared absorption with a quantum‐cascade laser instrument (QC‐TILDAS) [McManus et al., 2008]. NOx and NO2 were measured with the same CRD instrument described in section 2.2.1, which also measured O3 and NOy. O3 was measured by conversion to NO2 in excess NO and subsequent subtraction of ambient NO2 from the resulting total Ox [Washenfelder et al., 2011a]. NOy was thermally converted to NO or NO2 with a quartz heater (650°C) and quantitatively converted to NO2 via an addition of O3 [Wild et al., 2014]. The accuracy and detection limit of NOx, NO2, and O3 in 2014 were <5% and <50 pptv, respectively. The NOychannel had a limit of detection of <200 pptv and an accuracy of 12% based on recent field comparisons to other NOy instruments [Wild et al., 2014]. Conversion of NOy in a 650°C quartz oven may have also suffered interference from the unintended conversion of a small fraction (~6%) of NH3 in the presence of O3. Uncertainty associated with this artifact is estimated for the NOy data based on co‐located NH3 and O3 measurements, but data are not corrected for this potential interference.
In addition to carriage instruments, O3 lidar (NOAA TOPAZ) [Alvarez et al., 2011; Langford et al., 2015] measurements were made at a ground site ~0.5 km south of the main tower and j(NO2) photolysis frequencies (National Center for Atmospheric Research (NCAR) filter radiometer [Shetter et al., 2003]) were measured from a trailer parked at the tower base. Lastly, spectral surface albedo measurements derived from a visible (415–1625 nm) Multi‐Filter Rotating Shadowband Radiometer [Harrison et al., 1994; Michalsky and Hodges, 2013] were made from a NOAA Surface and Radiation Budget Monitoring mobile laboratory [Augustine et al., 2000] parked at BAO for the duration of the campaign.
2.3 Ozone Production Efficiency
Ozone production efficiencies were only derived from 2014 data due to the lack of NOymeasurements in 2012. Chemical observations were averaged to a 1 min time resolution and filtered to include data after noon (12–6 P.M. mountain daylight time (MDT)) during peak O3 production and sampling altitudes >25 m agl to reduce the influence of deposition to the surface. The slope of the Ox to NOz correlation at individual, 15 min intervals was used to isolate and derive the OPE of individual air parcels. In contrast to the O3/NOz slope defined in previous studies [e.g., Hirsch et al., 1996; Olszyna et al., 1994; Trainer et al., 1993, 1995], the use of Ox accounts for local O3 titration through reaction of NO with O3 near NOx emission sources. Additionally, NOz, instead of NOy, normalizes age across different air parcels [Trainer et al., 1993]. However, by not additionally accounting for NOy removal processes, such as surface deposition of individual NOy species (e.g., HNO3), the OPEs derived here are upper limits. Further OPE details are discussed in sections 3.3 and S2.
2.4 Box Model
2.4.1 Model Description and Constraints
Model simulations were performed with the Dynamically Simple Model of Atmospheric Chemical Complexity (DSMACC) [Emmerson and Evans, 2009]. DSMACC is a zero dimension box model that uses the Master Chemical Mechanism (MCM v3.3.1) for its chemistry scheme and the NCAR Tropospheric Ultraviolet and Visible Radiation Model (TUV v5.2) [Madronich et al., 1998] for photolysis rates. The MCM is a near‐explicit chemical mechanism representing the gas‐phase tropospheric degradation of VOCs [Jenkin et al., 2015]. The subset of chemistry used here includes a complete inorganic mechanism and degradation scheme for 50 primary VOCs, with a total of 4002 species and 15,555 reactions.
All DSMACC simulations are initialized at 8 A.M. MDT and integrated forward for 24 h with a 10 min time step. To represent the NFR as a photochemical box, simulations are initialized with and constrained every 30 min to SONNE diel average observations of temperature and mixing ratios of CO, CH4, 42 non‐methane VOCs, and water vapor (derived from 10 m relative humidity measurements). Simulations are constrained to 2012 data only due to lack of speciated VOC measurements in 2014. For comparison, both temperature and observed O3 mixing ratios were higher in 2012 than 2014 with differences in maximum diel averages (27 July to 12 August 2012 and 2014) of 1°C and 1.8 ppbv, respectively. DSMACC simulations were additionally constrained to SONNE diel average observations of total NOx, which was partitioned by the model into its components (NO and NO2) every 10 min assuming photo‐stationary state, using j(NO2), temperature, and O3. Section S3 provides further information on DSMACC constraints, which force the model to accurately represent primary species whose average concentrations are governed by processes not represented in the box model, such as emissions and horizontal transport.
An additional dilution rate constant was applied to all 4002 model compounds to simulate average vertical transport and loss from the box (as described in section S3.3). A dilution rate constant of 1.05 × 10−4 s−1 was derived from a fit of the model output to the diel average observations of 10 secondary products. These 10 secondary species consist of O3, six oxygenated VOCs, and three alkyl nitrates, which were initialized to their average observed values (8 A.M. MDT) but not otherwise constrained (see Figure S6). Background mixing ratios of these 10 compounds (Table S3) were also added to the model at the same rate of dilution to account for entrainment and mixing with the residual layer during boundary layer growth. At the fit dilution rate, the average model‐to‐observation difference for all 10 compounds was −13.7% (for individual compounds, see Table S3). Table S2 summarizes the model treatment of all chemical observations, and Figure 2 illustrates the observed non‐methane VOCs in terms of their diel average OHR.
2.4.2 Model Scenarios
Base (Case 1) simulations represent the average chemical composition at BAO and were constrained to diel average chemical and physical observations as described above. In comparison with Case 1 simulations, Cases 2 and 3 (described below) are used to quantify the impact of primary O&NG VOCs on maximum photochemical O3 production.
Case 2 simulations represent the average chemical composition at BAO without primary O&NG VOCs. To derive this VOC scenario, speciated fractions of primary VOCs emitted from O&NG activity were subtracted from Case 1 diel average observations. For example, propane was reduced by 90% between Cases 1 and 2, as 90% of observed propane at BAO was attributed to O&NG emissions [Gilman et al., 2013]. Table S2 provides a full list of speciated O&NG contribution factors. For all non‐methane VOCs, Gilman et al. [2013] used a multivariate regression with O&NG (propane) and urban (acetylene) tracers to derive O&NG factors. Oxygenated VOCs were not tightly correlated with either tracer and were not assigned an O&NG factor by Gilman et al. [2013]. Here the O&NG factors for these compounds have been set to 0%, resulting in a conservative (lower limit) estimate for the attributed O&NG fraction of observed VOCs. In addition, three cycloalkanes, two alkenes, one aldehyde, three biogenic VOCs, and three alkyl nitrates measured during SONNE were not reported by Gilman et al. [2013] and are also assigned an O&NG contribution of 0%. The cycloalkanes likely have an O&NG source but minimally impact simulated O3 due to their small mixing ratios (<0.03 ppbv) and nonexplicit representation in the MCM (section S3.1). For all additional species measured but not explicitly represented in the MCM (see section S3.1), factors were applied to each individual compound prior to lumping. As illustrated in Figure 3, primary O&NG emissions contributed to the majority of alkane OHR (87%) and carbon mixing ratio (86%), but <25% to all other VOC classes. For CH4, Pétron et al. [2014] quantified the O&NG contribution in the Wattenberg Gas Field as 75% using a combination of aircraft CH4 observations and a regional bottom‐up emission inventory (derived from literature emission factors, Colorado State inventory data, and EPA reported facility‐level emission estimates). Here 75% is applied to the observed average diel profile of enhanced CH4 (minus campaign background of 1814 ppbv). For NOx, no contribution from O&NG activity was assumed. County level NOx emissions based on the 2011 (v1) U.S. National Emissions Inventory (further details in section S4) suggest an O&NG contribution of 5.5% to NOx emissions in the NFR nonattainment area (Figure 1). Past work in a Utah O&NG basin has shown that NOx emissions from O&NG production can be overestimated by a factor of 4 [Ahmadov et al., 2015], indicating that the 5.5% contribution of O&NG activity to NFR NOxemissions may be an upper limit.
Case 3 simulations represent the average BAO chemical composition with a doubled contribution from primary O&NG VOCs. For Case 3, speciated factors for primary O&NG VOCs (as described above) were added to Case 1 diel observations. Table S4 provides a numeric comparison of all three VOC scenarios in terms of their non‐methane VOC OHR and carbon mixing ratio (ppbC).
3 Results and Discussion
3.1 Observed Chemical Composition and Wind Patterns
Air composition at BAO contains chemical tracers from all regional emission sectors (e.g., O&NG, urban, and agriculture), irrespective of wind direction. The histogram in Figure 4b plots simple chemical tracers for all major emission sectors (O&NG, CH4; agriculture, NH3; and urban, CO and NOx), averaged between 2012 and 2014 observation years, binned by wind direction, and normalized to westerly mixing ratios. Data have been binned by four wind directions and filtered to include wind speeds >2.5 m/s to minimize the influence of nearby emission sources and to be consistent with the threshold used by Pétron et al. [2012]. Figure 4b shows that air at the site has a substantial contribution from all regional emission sources irrespective of local north, east, or southerly wind directions. In addition, enhancements of tracers in the direction of each major emission source (e.g., NOx is slightly enhanced in southerly winds) are smaller than those observed in wintertime [Brown et al., 2013, Figure 7]. These observations suggest significant mixing and recirculation that causes regional air to have characteristics of all surrounding emission sectors.
Several mechanisms serve to mix emissions from different sources within the NFR. During summer, winds follow a typical mountain‐valley diel pattern. During the day, thermally driven upslope winds predominately flow from the east, with a slight southerly component [Toth and Johnson, 1985]. Beginning in late afternoon, flow patterns turn around and a westerly downslope occurs along the South Platte River Basin, often accompanied by afternoon regional thunderstorm activity [Toth and Johnson, 1985]. Figure 4a provides a histogram of 30 min averaged winds measured at BAO during July and August 2012 and 2014 (100 m winds, speed >2.5 m/s, 11 A.M. to 3 P.M. MDT), which illustrate this dominant afternoon easterly flow prior to the downslope switch. A terrain‐forced mesoscale vortex circulation pattern, termed the Denver Cyclone, is also a common occurrence during summer months [Crook et al., 1990; Szoke, 1991; Szoke et al., 1984; Wilczak and Glendening, 1988]. These complex circulation patterns combine to mix air parcels, making it difficult to model the daily evolution of emissions from spatially distinct regional emission source sectors.
3.2 VOC OH Reactivity: 2012
The campaign average (±1σ standard deviation) OHR for non‐methane VOCs (excluding HCHO) observed during SONNE was 2.4 ± 0.9 s−1. This compares to a previous determination at BAO of 3 ± 3 s−1 based on an analysis of data from same instrument in winter 2011 [Gilman et al., 2013] and summertime measurements near O&NG operations in Pennsylvania of 2.4 ± 1.4 s−1 (includes CH4, excludes HCHO) [Swarthout et al., 2015]. On average, alkanes were the dominant contributing class to VOC OHR (56%; Figure 2), of which the majority can be attributed to primary O&NG emissions (87%: VOC OHR, 86%: ppbC; Figure 3). Biogenic VOCs have been shown to dominate VOC OHR in O&NG regions in Pennsylvania (47 ± 22% [Swarthout et al., 2015]) and Texas (70% [Rutter et al., 2015]), but only contribute on average, 8% to VOC OHR at BAO. This result highlights the importance of O&NG emissions relative to biogenic emissions on O3 production in the NFR, making this location unique compared to two east/southeastern U.S. O&NG basins.
3.3 Ozone Production Efficiency: 2014
During the 2014 campaign, afternoon Ox was correlated with NOz (Figure 5), typical of summertime relationships between O3 and oxidized reactive nitrogen observed in other U.S. regions [e.g., Trainer et al., 1993]. Individual OPEs were derived from a two‐sided regression fit of Ox to NOz every 15 min between 12 and 6 P.M. MDT after removing time intervals with fewer than 11, 1 min data points. This time period was chosen to minimize the effects of nonphotochemical factors such as morning O3 entrainment (see section S2.3.2) and to compare the products of photochemistry (i.e., NOz and O3) in distinct air parcels. Increasing the time period to 9 A.M. to 6 P.M. MDT introduces additional scatter in the data from the OPE analyses but does not change the main conclusions presented below. Further, fits with intercepts more than ±2σ from the mean intercept were also removed, as described further below and in section S2.2. There were 305 OPE fits that met these criteria, which represent at least 15 min of 27 (87%) afternoons in 2014 (see Figure S1). The average (±1σ) of these 305 OPEs was 2.9 ± 4.4 ppbv/ppbv.
To ensure at least a 98.4% (i.e., significant) probability of correlation, a subset of these OPEs with correlation coefficients (r2) > 0.5 was also selected. There were 80 OPEs that met the r2threshold, which represent at least 15 min of 22 (71%) afternoons in 2014 (see Figure S1). The average (±1σ) of this 80 OPE subset was 5.3 ± 3.6 ppbv/ppbv. Selection of this subset reduced scatter in the data but also introduced a high bias by eliminating data scattered close to zero (e.g., with small changes in Ox and/or NOz). We take this smaller 80 OPE subset to represent time periods with the greatest photochemical O3 production but compare both 305 and 80 OPE populations below. Both populations are representative of the majority of high (>70 ppbv) O3 days observed at BAO in 2014.
Average OPEs (±1σ) derived here are similar to those from analyses in other regions of the U.S. However, many previous studies have defined OPE as the slope of O3/NOz or O3/NOy, making it difficult to directly compare values here to much of the past ~20 years of OPE literature. Nevertheless, the averages of 2.9 ppbv/ppbv (305 OPEs) and 5.3 ppbv/ppbv (80 OPEs) fall within the range of 2–8 ppbv/ppbv for Ox/NOz previously reported for urban regions across the U.S. [Kleinman et al., 2002; Nunnermacker et al., 1998; St. John et al., 1998; Zaveri et al., 2003].
3.3.1 NFR Emission Sector OPEs
To distinguish the influence of different emission sectors on OPE, individual OPEs were sorted according to two markers of air transport history: (1) wind direction and (2) simple chemical tracers. Sorting the data according to these markers contrasts the O3 production associated with the relatively different VOC composition and NOx mixing ratios of O&NG and urban emission sectors. As this section describes, however, any dependence of OPE on these parameters is considerably smaller than the variability and/or uncertainty in the observed OPE data.
3.3.1.1 OPE as a Function of Wind Direction
Air arriving at BAO from the northeast is expected to have traveled over regional O&NG operations, while that from the southeast to have been relatively more urban influenced (Figure 1). However, the OPE measured at BAO does not vary strongly with wind direction. The overall Ox/NOz correlation in 2014, colored by wind direction (Figure 5, left), does not show a clear difference in air arriving from south or north of the site. Analysis of individual OPEs against wind direction reveals similar results. Figure 6 provides a wind rose of the 80 OPE subset (r2 > 0.5) colored by northeast (NE: 0–90°), southeast (SE: 90–180°), and western (W: 180–360°) wind directions (15 min average). The box and whisker plots for NE and SE wind sectors show no statistically significant (i.e., p > α, α = 0.05) difference in their average (difference = 0.6 ppbv/ppbv, p = 0.43) or median (difference = 0.1 ppbv/ppbv, p = 0.88) values. In addition, there is no significant difference between average NE and SE OPEs (difference = 0.5 ppbv/ppbv, p = 0.39) when calculated from the larger population of 305 without the r2selection. Due to the large observed variability in OPE, 95% confidence intervals for differences in mean NE and SE OPEs are 0.6 ± 1.4 ppbv/ppbv and 0.5 ± 1.1 ppbv/ppbv for the 80 and 305 OPE populations, respectively. This wind direction analysis suggests a 95% probability that the OPE influence of O&NG emissions is less than 1.8 ppbv/ppbv. During times of high photochemical activity in 2014 (e.g., highest NOz mixing ratios; Figure 5), an OPE less than 1.8 ppbv/ppbv suggests that the O&NG sector contributes at most 11 ppbv to total O3. The actual O&NG influence determined from the box model analysis is likely considerably smaller (see section 3.4.2).
Due to the complexity of local air trajectories, including diel flow patterns that mix urban and O&NG emissions (section 3.1), it is difficult to accurately determine air transport and mixing histories using observed wind directions alone. A back trajectory model has the potential to track air transport history more accurately than local wind direction. As with observed wind direction, wind sectors for the 80 OPE subset as defined by a back trajectory model (described in section S5) show no statistically significant difference between average OPEs from the NE and SE wind sectors (difference = 0.6 ppbv/ppbv, p = 0.55; Figure S9).
The lack of statistically significant difference in observed OPE with observed or modeled wind direction is evidence for mixing between air parcels that obscures quantifiable differences between urban and O&NG sectors and/or an OPE effect from O&NG emissions that has a 95% probability of being less than 1.8 ppbv/ppbv. We interpret these results to mean either (1) observed OPEs are the product of both urban and O&NG emissions that were well‐mixed prior to measurement at BAO or (2) an OPE influence of urban and O&NG emissions that are similar enough (i.e., <1.8 ppbv/ppbv) to be obscured by mixing when air is transported to BAO. The box modeling results discussed in section 3.4.3 are consistent with the second scenario and suggest an O&NG influence on regional average OPE of 1.3 ppbv/ppbv.
3.3.1.2 OPE as a Function of Chemical Tracers
Chemical tracers provide an additional method to determine air transport history. In the NFR, CH4 is emitted primarily by O&NG (see above section 2.4.2), NH3 by agriculture, and CO and NOx by urban activity. Background mixing ratios (minimum observed campaign values) of CO (76 ppbv) and CH4 (1916 ppbv) were subtracted prior to analysis. Figure 7 shows correlations of the 80 OPE subset with NOx, NOy, NH3, CH4, and CO. Correlations visually indicate a slight decrease in OPE with increasing tracer mixing ratios; however, correlation coefficients indicate no statistically significant trend at the 95% confidence level (p > 0.05). Correlations between chemical tracers and the 305 OPE‐population are also insignificant (p > 0.22). These results indicate relatively well‐mixed air, also suggested by the dominant easterly flow and nondirectionally enhanced tracer mixing ratios shown in Figure 4.
A second possibility is that these tracers are not specific enough to their assigned emission sectors. As previously discussed in Pétron et al. [2012, 2014], approximately 25% of CH4emissions are not associated with O&NG operations, including three landfills located approximately 3 km to the south‐southwest of BAO. In addition, NEI‐2011 inventories attribute 27% of NOx emissions in Weld County (Figure 1) to O&NG operations (section S4). Ideally, this analysis would be conducted with more specific chemical tracers not available in 2014 (e.g., O&NG: propane and urban: acetylene) but suggests that simple tracers used here do not uniquely distinguish the influence of different emission sectors on observed OPE at BAO.
3.3.2 Uncertainty in OPE Analysis
Interpretation of the Ox/NOz relationship is subject to several limitations [Ryerson et al., 1998; Trainer et al., 1993] that are presented below in terms of their relation to deriving an average OPE under NFR conditions.
First, variability in background O3 may complicate OPE analysis [e.g., Neuman et al., 2009]. Backgrounds are represented by the intercept of the Ox/NOz correlation and will artificially change the OPE if one fit is applied to air parcels with different backgrounds (see example Figure S2). Therefore, OPE was derived from short time intervals (15 min) and filtered for intercepts greater than 2σ from the mean Ox background (further details in section S2.2) in order to isolate air parcels with similar O3 backgrounds.
Second, Ox is not always positively correlated with NOz. This is likely the result of (1) environmental conditions that do not promote photochemical activity and/or (2) transport processes that mix air parcels with differences in background O3 mixing ratios similar to their photochemical O3 enhancements. Summer 2014 in the NFR was unseasonably cool with high thunderstorm activity (Figure S5), which can enhance the downwind transport of O3 but also inhibit the stagnation and accumulation of pollution that contributes to OH radical generation and efficient O3 production. These environmental conditions can lead to periods of time with moderate photochemical activity and O3 production of only a few ppbv, similar to the variability observed in 2014 background O3 (Ox background: 56.7 ± 9.3 ppbv (2σ)). Mixing and/or sampling of these air parcels remove any observable Ox/NOzcorrelation. The r2 filter is applied to remove these events but by doing so, biases the 80‐OPE average high.
Third, OPE is sensitive to HNO3 deposition [e.g., Neuman et al., 2009; Sillman et al., 1998; Trainer et al., 1993]. Preferential removal of HNO3 relative to O3 will artificially raise the Ox/NOz slope since HNO3 is frequently the largest NOz component in summer. A deposition velocity of 1–5 cm s−1 within a 2 km boundary layer provides an upper limit estimate of 11–52% of NOz lost over 6 h of transport (further details, see section S2.3.1). Recalculating individual OPEs with corrected NOz produces an average (±1σ) OPE range of 3.3 ppbv/ppbv (±2.2) to 4.7 ppbv/ppbv (±3.2) for the highly correlated 80‐OPE subset, lower than the original average of 5.3 ppbv/ppbv but within the standard deviation of 3.6 ppbv/ppbv. Additional, unintended conversion of NH3 may also occur in the presence of ambient O3 at temperatures of 650°C in the CRD NOy quartz oven. Adjusting NOz measurements of the original 80 OPEs with concurrent NH3 and O3 measurements (further details in section S2.3.1) increases the average to 5.9 ppbv/ppbv. Combination of HNO3 and NH3 artifacts suggests that the average OPE for the highly correlated subset is between 3.3 and 5.9 ppbv/ppbv (−2.0/+0.6), a range encompassed by the standard deviation (1σ = 3.6 ppbv/ppbv) of the originally derived average.
An analysis of the 2014 data provides an average and expected distribution of observed OPE at BAO but does not distinguish the influence of urban emissions from the O&NG sector. This result does not change with the selection of highly correlated OPEs. These observations lead to three possible conclusions: (1) based on observational and modeled‐wind direction analyses, the OPE difference between O&NG and urban emission sectors has a 95% probability of being within 1.8 ppbv/ppbv; (2) OPE differences are obscured by regional air mixing; and/or (3) small OPE differences cannot be distinguished using simple chemical tracers with multiple emission sources. Although OPE does not statistically vary with either wind direction or chemical tracer analyses, the similarity provides a point of comparison between the observations and box model simulations described below.
3.4 Box Model Simulations: Maximum Photochemical O3
Model simulations were constrained to SONNE diel average observations as described in section 2.4.1. With the dilution rate constant derived from a fit to 10 secondary species, the Case 1 VOC scenario simulates maximum O3 to within −2.5% (−1.7 ppbv) of the SONNE diel average. The average relative deviation between the model output and observations for O3is −2.6% (11 A.M. to 3 P.M. MDT, ±2 h from solar noon). Figure 8 illustrates the observed diel average and model output for O3, which suggests an accurate base case simulation of maximum O3 produced at BAO. Deviation between simulated and observed O3 profiles after 4 P.M. MDT is the result of the constant dilution/background‐O3 entrainment rate that is applied to the entire 24 h simulation, as described in section S3.3.
3.4.1 NOx Sensitivity
Case 1 simulations were run while constrained to SONNE observed mixing ratios of VOCs, NOx, and temperature (as described in section 2.4). To test the sensitivity of maximum photochemical O3 to NOx, 11 simulations were run with the Case 1 VOC scenario, scaling observed NOx mixing ratios (displayed in Figure 2) by a factor of 0 to 5. As shown in Figure 9and Table S6, observed SONNE NOx mixing ratios (NOx scaling factor = 1) produce a maximum of 16.7 ppbv of photochemical O3, while doubling observed NOx increases photochemical O3 to 19.5 ppbv. Here photochemical O3 is defined as the difference between simulated O3 and the simultaneous mixing ratio of O3 in the zero‐NOx simulation. Photochemical O3 production does not occur without NOx; however, O3 is introduced to the model to simulate entrainment of background O3 into the boundary layer (see section S3.3). O3 entrainment occurs at the same rate in each simulation and is therefore represented by the zero‐NOx simulation. Subtracting these mixing ratios from each nonzero‐NOx simulation provides the photochemically produced O3 for the given amount of NOx.
Figure 9 suggests that photochemical O3 production in the region surrounding BAO is NOxlimited. An increase or decrease in NOx by a factor of 2 leads to a 16.8% (±2.8 ppbv) change in maximum photochemical O3. However, NOx increases above a factor of 2 move O3production into the NOx saturated (VOC sensitive) photochemical regime, such that further increases will reduce maximum O3. These results are consistent with NOx sensitivities derived from previous 3‐D modeling of NFR O3 with a 2010 emission scenario [Colorado Department of Public Health and Environment (CDPHE), 2008].
3.4.2 O&NG Influence
To determine the average influence of O&NG emissions on maximum photochemical O3, the fraction of VOCs attributed to primary O&NG emissions was removed (Case 2) and doubled (Case 3) as described in section 2.4.2. Twenty‐two additional simulations were run with these two VOC scenarios while scaling SONNE observed NOx mixing ratios between 0 and 5. Results of these simulations in comparison to Case 1 from Figure 9 are listed in Table S6 and shown in Figure 10. The pie chart inserts represent the 24 h average, non‐methane VOC‐class fractional contribution to VOC OHR and carbon mixing ratio (ppbC) for each VOC scenario. At observed NOx mixing ratios, the difference in maximum photochemical O3between Cases 1 and 2 (no O&NG VOC emission contribution) is 17.4%, or a 2.9 ppbv decrease. Similar to maximum O3, the O&NG VOC influence on photochemical MDA8 (maximum daily 8 h average) is a decrease of 18.4% or 2.5 ppbv. Doubling the mixing ratio of O&NG VOCs increases simulated maximum photochemical O3 by 13.2% or 2.2 ppbv, indicating a nonlinear change in O3 with O&NG VOCs. These three VOC scenarios suggest that while O3 production is sensitive to NOx, maximum and MDA8 O3 mixing ratios will also respond to reductions in O&NG VOCs, again consistent with previous 3‐D model results [Colorado Department of Public Health and Environment (CDPHE), 2008].
The 16.7 ppbv of photochemical O3 produced in Case 1 represents the maximum O3enhancement under average conditions. However, 2014 observed O3 mixing ratios (Figures 5 and S1) show that O3 enhancements above background can be approximately 30 ppbv on days with high photochemical activity (e.g., high O3 and NOz mixing ratios). As described below in section 3.4.4 and section S7, model sensitivity studies show that photochemical O3is highly sensitive to photolysis rates, potentially explaining the large enhancements on days with photolysis rates larger than average values. In contrast, the O&NG VOC contribution to O3 (~20%) is not highly sensitive to photolysis rates (section 3.4.4). Therefore, assuming mixing ratios of VOCs and NOx similar to their observed diel average values, the absolute contribution from O&NG VOCs could be ~6 ppbv on photochemically active days with ~30 ppbv of regional photochemical O3 production.
The total contribution of O&NG activity to photochemical O3 will depend on emissions of NOx as well as VOCs. The difference of 17.4% highlighted in Figure 10 assumes no change in NOx from observed mixing ratios. Applying NOx reductions of 5.5% based on EPA NEI‐2011 inventories (see section 2.4.2), Cases 1 and 2 suggest that O&NG activity contributes 18.6% (3.1 ppbv) to maximum photochemical O3, in comparison to 17.4% (2.9 ppbv) from VOC emissions alone. However, NEI inventory estimates of O&NG NOx emissions may be overestimated [e.g., Ahmadov et al., 2015]. Thus, the total O&NG contribution to modeled maximum photochemical O3 at diel average mixing ratios of NOx and VOCs is between 17.4 and 18.6% or 2.9 and 3.1 ppbv.
As shown in Figure 10, alkanes contributed 82% to the average SONNE non‐methane carbon mixing ratio measured at BAO (Figure 10, pie chart), of which 86% are attributed to O&NG emissions (Figure 3). Despite this dominant fraction, the alkane contribution to average non‐methane VOC OHR was 56% and less than 18% to maximum photochemical O3. This result is consistent with previous literature showing that alkanes are not efficient at producing O3[e.g., Russell et al., 1995] and demonstrates the difficulty in using either carbon mixing ratio or VOC OHR for attribution of photochemically produced O3 to O&NG VOC emissions.
Despite evidence for reasonably well‐mixed urban and O&NG emissions, Figure 1 suggests spatial heterogeneity in emissions from these sources, which can result in different photochemical regimes for O3 production. For example, NOx and urban VOCs are expected in larger concentrations ~30 km south of BAO near urban‐Denver [Brown et al., 2013; Swarthout et al., 2013], while O&NG VOCs may be larger ~50 km north of BAO centered in the Wattenberg Gas Field near Greeley [Swarthout et al., 2013]. However, O3 levels in exceedance of NAAQS occur across these same distances [Colorado Department of Public Health and Environment (CDPHE), 2015], suggesting a level of regional similarity in O3 enhancements. Buffering effects in the VOC‐NOx sensitivity curves (Figure 11) can explain regional O3enhancements despite different photochemical regimes. Figure 11 shows that if, for example, the absolute mixing ratios of non‐O&NG VOCs remained the same but O&NG VOCs were doubled and NOx reduced by 30% (Figure 11, red diamond), the model predicts the same maximum O3 produced as that at BAO. This implies that north of BAO, with a potentially larger abundance of O&NG VOCs, this region would be more sensitive to NOxemission reductions. In contrast, if the non‐O&NG VOCs remained the same but O&NG VOCs were reduced by 50% at two southern locations, the model predicts nearly the same maximum O3 produced for NOx emissions 1.3 to 2.7× higher than those at BAO (Figure 11, red box and triangle). O3 production at the second of these points (red triangle) is in the NOx‐saturated regime but still produces the same photochemical O3 as at the NOx‐limited BAO. These scenarios suggest that O3 enhancements in the NFR can be regional, while effective control strategies should still be informed by finer scale VOC/NOx observations.
3.4.3 Model Ozone Production Efficiency
Case 1 and 2 simulations, as described above, were used to calculate the influence of O&NG VOC emissions on modeled OPEs. The OPE of each model simulation was calculated from the average ΔOx/ΔNOz ratio between 11 A.M. to 3 P.M. MDT, the same time period during which the model was fit to best reproduce 10 secondary products (see section S3.3). Here ΔOx and ΔNOz are used to capture photochemical O3 production and NOx oxidation. Delta Ox and ΔNOz are defined as the difference between the Ox and NOz mixing ratios in a given simulation and the simultaneous values in the zero‐NOx simulation (as described in section 3.4.1). The HNO3 dilution rate constant was the same as all other 4001 species (k = 1.05 × 10−4 s−1), but its deposition rate was set to 0 s−1 to remove the influence of HNO3 loss on NOz. Eliminating HNO3 deposition does not impact simulated maximum photochemical O3but does increase Case 1 OPE by 7.7% (see Table 1). Simulated OPE was also found to decrease with increasing NOx mixing ratios, consistent with previous OPE model simulation results [e.g., Lin et al., 1988].
| Parameter | Base Case Value | Value Adjustment | Δ Max Photochemical O3 | Δ OPE (%)b | |
|---|---|---|---|---|---|
| (ppbv) | (%)b | ||||
| Photolysis rates | 8 A.M. MDT values:
j(NO2) = 3.6 × 10−3 s−1 j(O1D) = 4.0 × 10−6 s−1 |
±25% | +6.0/−5.4 | +35.9/−32.3 | +4.6/−3.1 |
| ±10% | ±2.3 | ±13.8 | ±1.5 | ||
| Dilution | k = 1.05 × 10−4 s−1 | ±10% | +1.3/−1.1 | +7.8/−7.1 | ‐ |
| Temperature | SONNE observations | ±10% | ±0.8 | ±4.8 | +7.7/−6.2 |
| Background O3 | 58 ppbv | ±10% | ±0.1 | ±0.6 | ±3.1 |
| Enhanced CH4 | SONNE observations‐background | ±10% | ±<0.1 | ‐ | ‐ |
| ±75% | ±0.1 | ±0.6 | ‐ | ||
| O3 deposition | k = 3.5 × 10−6 s−1 | ±10% | ±<0.1 | ‐ | ‐ |
| Albedo | 0.067 | ±10% | ±<0.1 | ‐ | ‐ |
| HNO3deposition | k = 2.2 × 10−5 s−1 | −100% | − <0.1 | ‐ | +7.7c |
- a Calculated for Case 1 VOC scenario at NOx scaling factor = 1.
- b Values not provided if change is <0.1 ppbv (max photochemical O3) or <0.1 ppbv/ppbv (OPE).
- c HNO3 deposition artificially increases modeled OPE by 7.7%; all OPE simulations were run without HNO3 deposition as this does not change photochemical O3 and is necessary to accurately model OPE.
At a NOx scaling factor of 1, OPEs derived from the Case 1 and 2 VOC scenarios are 6.5 ppbv/ppbv (±0.5) and 5.2 ppbv/ppbv (±0.5), respectively. The errors are derived from the quadrature addition of OPE uncertainties associated with model parameters listed in Table 1(not including HNO3 deposition). These results suggest that O&NG VOC emissions increase the efficiency of O3 production at BAO by 1.3 ppbv/ppbv (20%). To account for NEI‐estimated O&NG NOx emissions (see section 2.4.2), the OPE for Case 2 (no O&NG VOCs) was calculated at a NOx scaling factor of 0.945 (−5.5%). The OPE influence of O&NG emissions did not change, as this small NOx reduction did not influence the simulated OPE by >0.1 ppbv/ppbv. The similarity between Cases 1 and 2 suggests that the OPE influence of O&NG emissions is small enough to be obscured in observations at BAO due to air transport and mixing, as discussed previously in section 3.3.1.1.
3.4.4 Model Sensitivity Studies
As described above, simulations for all three VOC scenarios were constrained every 30 min to chemical species and physical parameters. The only tunable model parameter was the dilution rate constant, which was derived by minimizing the deviation between observations and model output for 10 select secondary products, including O3. A ±10% change in the dilution rate constant changes simulated maximum photochemical O3 in Case 1 by +1.3/−1.1 ppbv (+7.8/−6.6%) and average model‐to‐observation relative deviation of all 10 compounds by +1.6/−1.2%.
Case 1 (at observed NOx mixing ratios) was additionally tested for sensitivities to ±10% changes in other model constraints including photolysis rates, albedo, temperature, background O3, enhanced CH4, and O3/HNO3 deposition rates (see Table 1). Sensitivity differences between maximum photochemical O3 and OPE can be explained by the additional dependence of OPE on photochemical NOx oxidation. Of the parameters tested, OPE is most sensitive to changes in temperature, while maximum photochemical O3 is most sensitive to changes in photolysis rates (more than the dilution rate constant) and increases by as much as 13.8% with a 10% increase in both j(NO2) and j(O1D) scaling factors. Observations of j(NO2) and j(O1D) are not known with greater than 25% accuracy, which will change absolute maximum photochemical O3 by +6.0/−5.4 ppbv (+35.9/−32.3%). To test the sensitivity of the main box model results to photolysis rates, 15 additional simulations were run for Cases 1 and 2, while scaling photolysis rates by ±25%. The difference in maximum photochemical O3 between Cases 1 and 2 is not as sensitive to changes in photolysis as is the absolute maximum simulated O3 (Figures S10 and S11). In other words, regardless of 25% changes in photolysis rates, BAO photochemical O3 remains sensitive to NOx (Figure S10) and the O&NG VOC influence ranges from 15.1 to 19.4% (Figure S11), within 2.3% of 17.4% derived under original photolysis conditions.
4 Conclusions
The Northern Front Range of Colorado has been in nonattainment with the NAAQS for O3since 2007. Summertime photochemical O3 in the NFR is influenced by regional NOxemissions, concentrated around urban‐Denver, and large VOC emissions from a rapidly developing O&NG basin. The BAO site lies between these major regional emission sectors and exhibits influence from each (O&NG, urban, and agriculture). Data from this site were used to quantify the influence of O&NG emissions on O3 production using an observationally constrained box model and metrics of VOC OHR and OPE.
OPEs derived from 2014 Ox/NOz correlations at 15 min time intervals during 27 afternoons have an average of 2.9 ± 4.4 ppbv/ppbv (1σ) for all determinations and 5.3 ± 3.6 ppbv/ppbv (−2.0/+0.6) for a smaller subset with high correlation between Ox and NOz. A difference in average OPE could not be statistically distinguished for air primarily influenced by O&NG and urban emissions using observed wind direction, modeled back‐trajectories, or simple chemical tracers. These results suggest that the OPE influence of O&NG and urban emissions at BAO is obscured by air mixing and/or do not differ to within 1.8 ppbv/ppbv. The simulated OPE difference of 1.3 ppbv/ppbv with and without O&NG primary VOCs falls within the uncertainty of the 2014 observational analyses.
Box model simulations constrained to diel average chemical and physical observations indicate that maximum photochemical O3 at BAO is NOx sensitive. Simulations with removed and doubled primary O&NG VOC contributions showed that O&NG VOC emissions contribute on average 17.4% (2.9 ppbv) to maximum photochemical O3 and scale nonlinearly with changes in O&NG VOCs. NEI emissions of O&NG NOx are estimated to contribute up to an additional 1.2% (0.20 ppbv) to the total contribution of O&NG activity to maximum O3photochemically produced at BAO. Alkanes contributed on average 82% to the observed carbon mixing ratio, of which 86% could be attributed to O&NG emissions. However, alkanes only contributed 56% to VOC OHR and less than 18% to modeled maximum photochemical O3.
Future work in the NFR is required to address several key uncertainties. First, detailed multiyear studies are required to assess the influence of rapid changes in O&NG and urban activities on ambient levels of VOCs and NOx and the sensitivity of photochemical O3production. Between 2012 and 2014, the number of active wells in Weld County increased by ~2000, oil production more than doubled, and natural gas production increased by a factor of ~1.6 [Colorado Oil and Gas Conservation Commission (COGCC), 2/2016]. Since early 2015, O&NG drilling activity has declined nationwide. In addition, the NFR population has increased by 12% to over 3 million people since 2010 [United States Department of Agriculture (USDA), 2016] and continues to grow, influencing the absolute emissions of NOx and distribution across the region. Such rapid changes in O&NG activity and urban development suggest the potential for year‐to‐year changes in photochemical O3 sensitivities and emissions of VOC and NOx.
Second, spatially distributed studies from across the region are required to understand the differences in O3 sensitivities in the more VOC impacted areas to the north and NOximpacted areas to the south. Analysis of recent 2014 and 2015 field studies should be informative. Future studies incorporating the type of detailed measurements and models presented here at ground sites that span the NFR would serve to improve the understanding of regional O3 production sensitivities to VOCs and NOx, as well address recent trends in emissions of both urban and O&NG NOx and VOCs.
List of Primary Acronyms
-
- BAO –
-
- Boulder Atmospheric Observatory
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- CRD –
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- Cavity Ring Down
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- DISCOVER‐AQ –
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- Deriving Information on Surface Conditions from Column and Vertically Resolved Observations Relevant to Air Quality
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- DSMACC –
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- Dynamically Simple Model of Atmospheric Chemical Complexity
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- EPA –
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- Environmental Protection Agency
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- FRAPPÉ –
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- Front Range Air Pollution and Photochemistry Éxperiment
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- MCM –
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- Master Chemical Mechanism
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- MDA8 –
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- Maximum Daily 8‐h Average
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- MDT –
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- Mountain Daylight Time
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- NAAQS –
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- National Ambient Air Quality Standard
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- NEI –
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- National Emission Inventory
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- NFR –
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- Northern Front Range
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- O&NG –
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- Oil and Natural Gas
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- OHR –
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- VOC OH Reactivity
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- OPE –
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- Ozone Production Efficiency
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- ppbC –
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- parts per billion of Carbon
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- SONNE –
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- Summer Ozone Near Natural gas Emissions
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- TUV –
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- Tropospheric Ultraviolet and Visible radiation model
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- VOC –
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- Volatile Organic Compound
Acknowledgments
This work was supported by NOAA’s Atmospheric Chemistry, Carbon Cycle, and Climate Program. We thank Rebecca S. Hornbrook, Eric C. Apel, and Alan J. Hills for TOGA data from FRAPPÉ 2014 and comments during the manuscript preparation process. We thank Betsy Weatherhead for her contribution to the statistical analysis. We also thank Patrick Reddy for insightful comments and discussion during preparation and Frank Flocke and Gabriele Pfister for FRAPPÉ campaign organization. Emily V. Fischer acknowledges support from the Colorado Department of Public Health and the Environment (CDPHE). Meteorological data from the Boulder Atmospheric Observatory (2012 and 2014) are available at http://www.esrl.noaa.gov/psd/technology/bao/ and http://www.esrl.noaa.gov/gmd/dv/data/?category = Ozone&site = BAO, SONNE data available at http://esrl.noaa.gov/csd, FRAPPÉ data are available at http://www‐air.larc.nasa.gov, NASA OMI total O3 column available at http://mirador.gsfc.nasa.gov, and CalNEX CO and VOCs are available at http://esrl.noaa.gov/csd. All referenced supplemental text, figures, and tables can be found in the supporting information.
